Decision Trees and Random Forests (Wildfire cause prediction)#
In this lesson, we will learn about decision trees and random forests and how they can be used for supervised machine learning tasks such as classification. A decision tree is an algorithm that can be used to determine how to classify or predict a target by making sequential decisions about the values of different features associated with a sample. Random forests use the ensemble vote of many decision trees to classify or predict a value.
We will use the data set from the paper “Inference of Wildfire Causes From Their Physical, Biological, Social and Management Attributes” by Pourmohamad et al., Earth’s Future, 2025. In this paper, they explored whether its possible to determine the cause of a wildfire (in cases where the cause is unknown) based on data from other wildfires where the cause was known.
References:
[1] Pourmohamad, Y., Abatzoglou, J. T., Fleishman, E., Short, K. C., Shuman, J., AghaKouchak, A., et al. (2025). Inference of wildfire causes from their physical, biological, social and management attributes. Earth’s Future, 13, e2024EF005187. https://doi.org/10.1029/2024EF005187
[2] Pourmohamad, Y., Abatzoglou, J. T., Belval, E. J., Fleishman, E., Short, K., Reeves, M. C., Nauslar, N., Higuera, P. E., Henderson, E., Ball, S., AghaKouchak, A., Prestemon, J. P., Olszewski, J., and Sadegh, M.: Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset, Earth Syst. Sci. Data, 16, 3045–3060, https://doi.org/10.5194/essd-16-3045-2024, 2024.
[3] Pourmohamad, Y. (2024). Inference of Wildfire Causes from Their Physical, Biological, Social and Management Attributes (0.1). Zenodo. https://doi.org/10.5281/zenodo.11510677
import pandas as pd
import seaborn as sns
import os
import numpy as np
Load in the data set#
The data set can be downloaded from “https://zenodo.org/records/11510677”.
!wget "https://zenodo.org/records/11510677/files/FPA_FOD_west_cleaned.csv" data/.
--2025-08-12 10:13:49-- https://zenodo.org/records/11510677/files/FPA_FOD_west_cleaned.csv
Resolving zenodo.org (zenodo.org)...
188.185.48.194, 188.185.45.92, 188.185.43.25
Connecting to zenodo.org (zenodo.org)|188.185.48.194|:443... connected.
HTTP request sent, awaiting response...
200 OK
Length: 139402360 (133M) [text/plain]
Saving to: ‘FPA_FOD_west_cleaned.csv.8’
FPA_FOD_w 0%[ ] 0 --.-KB/s
FPA_FOD_we 0%[ ] 106.59K 423KB/s
FPA_FOD_wes 0%[ ] 296.50K 640KB/s
FPA_FOD_west 0%[ ] 695.75K 948KB/s
FPA_FOD_west_ 0%[ ] 1.16M 1.16MB/s
FPA_FOD_west_c 1%[ ] 1.58M 1.25MB/s
FPA_FOD_west_cl 1%[ ] 1.93M 1.25MB/s
FPA_FOD_west_cle 1%[ ] 2.19M 1.21MB/s
FPA_FOD_west_clea 1%[ ] 2.55M 1.23MB/s
FPA_FOD_west_clean 2%[ ] 2.89M 1.23MB/s
FPA_FOD_west_cleane 2%[ ] 3.27M 1.25MB/s
PA_FOD_west_cleaned 2%[ ] 3.78M 1.31MB/s
A_FOD_west_cleaned. 3%[ ] 4.09M 1.30MB/s eta 99s
_FOD_west_cleaned.c 3%[ ] 4.38M 1.28MB/s eta 99s
FOD_west_cleaned.cs 3%[ ] 4.67M 1.27MB/s eta 99s
OD_west_cleaned.csv 3%[ ] 4.97M 1.32MB/s eta 99s
D_west_cleaned.csv. 3%[ ] 5.29M 1.34MB/s eta 1m 42s
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est_cleaned.csv.8 4%[ ] 6.29M 1.25MB/s eta 1m 42s
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cleaned.csv.8 5%[> ] 7.62M 1.25MB/s eta 1m 41s
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eaned.csv.8 6%[> ] 8.21M 1.18MB/s eta 1m 41s
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ned.csv.8 6%[> ] 8.91M 1.21MB/s eta 1m 41s
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d.csv.8 7%[> ] 9.57M 1.23MB/s eta 1m 40s
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F 9%[> ] 12.36M 1.27MB/s eta 97s
FP 9%[> ] 12.70M 1.28MB/s eta 97s
FPA 9%[> ] 13.03M 1.29MB/s eta 97s
FPA_ 10%[=> ] 13.33M 1.27MB/s eta 96s
FPA_F 10%[=> ] 13.62M 1.26MB/s eta 96s
FPA_FO 10%[=> ] 13.93M 1.25MB/s eta 96s
FPA_FOD 10%[=> ] 14.28M 1.25MB/s eta 96s
FPA_FOD_ 10%[=> ] 14.61M 1.25MB/s eta 95s
FPA_FOD_w 11%[=> ] 14.95M 1.25MB/s eta 95s
FPA_FOD_we 11%[=> ] 15.37M 1.26MB/s eta 95s
FPA_FOD_wes 11%[=> ] 15.77M 1.27MB/s eta 95s
FPA_FOD_west 12%[=> ] 16.15M 1.28MB/s eta 93s
FPA_FOD_west_ 12%[=> ] 16.54M 1.31MB/s eta 93s
FPA_FOD_west_c 12%[=> ] 16.94M 1.31MB/s eta 93s
FPA_FOD_west_cl 13%[=> ] 17.37M 1.33MB/s eta 93s
FPA_FOD_west_cle 13%[=> ] 17.66M 1.33MB/s eta 91s
FPA_FOD_west_clea 13%[=> ] 18.08M 1.36MB/s eta 91s
FPA_FOD_west_clean 13%[=> ] 18.50M 1.40MB/s eta 91s
FPA_FOD_west_cleane 14%[=> ] 18.86M 1.41MB/s eta 91s
PA_FOD_west_cleaned 14%[=> ] 19.14M 1.39MB/s eta 89s
A_FOD_west_cleaned. 14%[=> ] 19.47M 1.38MB/s eta 89s
_FOD_west_cleaned.c 14%[=> ] 19.84M 1.40MB/s eta 89s
FOD_west_cleaned.cs 15%[==> ] 20.17M 1.38MB/s eta 89s
OD_west_cleaned.csv 15%[==> ] 20.61M 1.39MB/s eta 87s
D_west_cleaned.csv. 15%[==> ] 20.98M 1.39MB/s eta 87s
_west_cleaned.csv.8 16%[==> ] 21.29M 1.36MB/s eta 87s
west_cleaned.csv.8 16%[==> ] 21.62M 1.33MB/s eta 87s
est_cleaned.csv.8 16%[==> ] 22.11M 1.35MB/s eta 86s
st_cleaned.csv.8 17%[==> ] 22.61M 1.40MB/s eta 86s
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leaned.csv.8 18%[==> ] 24.17M 1.42MB/s eta 83s
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d.csv.8 19%[==> ] 25.99M 1.43MB/s eta 82s
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F 21%[===> ] 28.89M 1.35MB/s eta 80s
FP 22%[===> ] 29.33M 1.36MB/s eta 80s
FPA 22%[===> ] 29.73M 1.38MB/s eta 78s
FPA_ 22%[===> ] 30.06M 1.37MB/s eta 78s
FPA_F 22%[===> ] 30.48M 1.38MB/s eta 78s
FPA_FO 23%[===> ] 30.85M 1.39MB/s eta 78s
FPA_FOD 23%[===> ] 31.20M 1.39MB/s eta 77s
FPA_FOD_ 23%[===> ] 31.59M 1.42MB/s eta 77s
FPA_FOD_w 24%[===> ] 31.93M 1.42MB/s eta 77s
FPA_FOD_we 24%[===> ] 32.27M 1.39MB/s eta 77s
FPA_FOD_wes 24%[===> ] 32.57M 1.36MB/s eta 76s
FPA_FOD_west 24%[===> ] 33.13M 1.43MB/s eta 76s
FPA_FOD_west_ 25%[====> ] 33.55M 1.44MB/s eta 76s
FPA_FOD_west_c 25%[====> ] 33.90M 1.43MB/s eta 76s
FPA_FOD_west_cl 25%[====> ] 34.32M 1.42MB/s eta 74s
FPA_FOD_west_cle 26%[====> ] 34.74M 1.45MB/s eta 74s
FPA_FOD_west_clea 26%[====> ] 35.11M 1.42MB/s eta 74s
FPA_FOD_west_clean 26%[====> ] 35.50M 1.44MB/s eta 74s
FPA_FOD_west_cleane 26%[====> ] 35.83M 1.41MB/s eta 73s
PA_FOD_west_cleaned 27%[====> ] 36.24M 1.44MB/s eta 73s
A_FOD_west_cleaned. 27%[====> ] 36.59M 1.44MB/s eta 73s
_FOD_west_cleaned.c 27%[====> ] 36.92M 1.43MB/s eta 73s
FOD_west_cleaned.cs 28%[====> ] 37.23M 1.41MB/s eta 72s
OD_west_cleaned.csv 28%[====> ] 37.53M 1.41MB/s eta 72s
D_west_cleaned.csv. 28%[====> ] 37.95M 1.40MB/s eta 72s
_west_cleaned.csv.8 28%[====> ] 38.26M 1.37MB/s eta 72s
west_cleaned.csv.8 29%[====> ] 38.64M 1.34MB/s eta 71s
est_cleaned.csv.8 29%[====> ] 38.95M 1.32MB/s eta 71s
st_cleaned.csv.8 29%[====> ] 39.43M 1.36MB/s eta 71s
t_cleaned.csv.8 29%[====> ] 39.79M 1.35MB/s eta 71s
_cleaned.csv.8 30%[=====> ] 40.20M 1.35MB/s eta 69s
cleaned.csv.8 30%[=====> ] 40.60M 1.36MB/s eta 69s
leaned.csv.8 30%[=====> ] 40.94M 1.36MB/s eta 69s
eaned.csv.8 31%[=====> ] 41.44M 1.39MB/s eta 69s
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sv.8 33%[=====> ] 44.08M 1.46MB/s eta 67s
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F 34%[=====> ] 45.73M 1.39MB/s eta 65s
FP 34%[=====> ] 46.12M 1.37MB/s eta 65s
FPA 34%[=====> ] 46.42M 1.31MB/s eta 65s
FPA_ 35%[======> ] 46.82M 1.30MB/s eta 65s
FPA_F 35%[======> ] 47.20M 1.30MB/s eta 65s
FPA_FO 35%[======> ] 47.60M 1.32MB/s eta 63s
FPA_FOD 36%[======> ] 47.92M 1.32MB/s eta 63s
FPA_FOD_ 36%[======> ] 48.18M 1.29MB/s eta 63s
FPA_FOD_w 36%[======> ] 48.43M 1.28MB/s eta 63s
FPA_FOD_we 36%[======> ] 48.82M 1.30MB/s eta 63s
FPA_FOD_wes 37%[======> ] 49.21M 1.31MB/s eta 63s
FPA_FOD_west 37%[======> ] 49.51M 1.27MB/s eta 63s
FPA_FOD_west_ 37%[======> ] 49.88M 1.28MB/s eta 63s
FPA_FOD_west_c 37%[======> ] 50.32M 1.33MB/s eta 62s
FPA_FOD_west_cl 38%[======> ] 50.77M 1.36MB/s eta 62s
FPA_FOD_west_cle 38%[======> ] 51.11M 1.37MB/s eta 62s
FPA_FOD_west_clea 38%[======> ] 51.49M 1.37MB/s eta 62s
FPA_FOD_west_clean 38%[======> ] 51.80M 1.35MB/s eta 60s
FPA_FOD_west_cleane 39%[======> ] 52.09M 1.33MB/s eta 60s
PA_FOD_west_cleaned 39%[======> ] 52.49M 1.32MB/s eta 60s
A_FOD_west_cleaned. 39%[======> ] 52.86M 1.34MB/s eta 60s
_FOD_west_cleaned.c 39%[======> ] 53.17M 1.35MB/s eta 59s
FOD_west_cleaned.cs 40%[=======> ] 53.47M 1.33MB/s eta 59s
OD_west_cleaned.csv 40%[=======> ] 53.81M 1.32MB/s eta 59s
D_west_cleaned.csv. 40%[=======> ] 54.20M 1.33MB/s eta 59s
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west_cleaned.csv.8 41%[=======> ] 54.87M 1.30MB/s eta 59s
est_cleaned.csv.8 41%[=======> ] 55.18M 1.26MB/s eta 59s
st_cleaned.csv.8 41%[=======> ] 55.70M 1.31MB/s eta 59s
t_cleaned.csv.8 42%[=======> ] 55.99M 1.29MB/s eta 57s
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cleaned.csv.8 42%[=======> ] 56.77M 1.33MB/s eta 57s
leaned.csv.8 43%[=======> ] 57.19M 1.34MB/s eta 57s
eaned.csv.8 43%[=======> ] 57.58M 1.35MB/s eta 56s
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ned.csv.8 43%[=======> ] 58.27M 1.37MB/s eta 56s
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d.csv.8 44%[=======> ] 59.00M 1.37MB/s eta 55s
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csv.8 44%[=======> ] 59.72M 1.38MB/s eta 55s
sv.8 45%[========> ] 60.08M 1.39MB/s eta 55s
v.8 45%[========> ] 60.47M 1.36MB/s eta 54s
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8 46%[========> ] 61.26M 1.41MB/s eta 54s
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F 46%[========> ] 62.03M 1.37MB/s eta 53s
FP 46%[========> ] 62.45M 1.39MB/s eta 53s
FPA 47%[========> ] 62.81M 1.39MB/s eta 53s
FPA_ 47%[========> ] 63.17M 1.39MB/s eta 53s
FPA_F 47%[========> ] 63.53M 1.40MB/s eta 52s
FPA_FO 48%[========> ] 63.95M 1.41MB/s eta 52s
FPA_FOD 48%[========> ] 64.29M 1.40MB/s eta 52s
FPA_FOD_ 48%[========> ] 64.73M 1.42MB/s eta 52s
FPA_FOD_w 48%[========> ] 65.07M 1.43MB/s eta 50s
FPA_FOD_we 49%[========> ] 65.41M 1.41MB/s eta 50s
FPA_FOD_wes 49%[========> ] 65.77M 1.39MB/s eta 50s
FPA_FOD_west 49%[========> ] 66.22M 1.42MB/s eta 50s
FPA_FOD_west_ 50%[=========> ] 66.68M 1.42MB/s eta 49s
FPA_FOD_west_c 50%[=========> ] 67.11M 1.44MB/s eta 49s
FPA_FOD_west_cl 50%[=========> ] 67.42M 1.42MB/s eta 49s
FPA_FOD_west_cle 50%[=========> ] 67.75M 1.41MB/s eta 49s
FPA_FOD_west_clea 51%[=========> ] 68.17M 1.43MB/s eta 48s
FPA_FOD_west_clean 51%[=========> ] 68.47M 1.42MB/s eta 48s
FPA_FOD_west_cleane 51%[=========> ] 68.78M 1.40MB/s eta 48s
PA_FOD_west_cleaned 51%[=========> ] 69.08M 1.36MB/s eta 48s
A_FOD_west_cleaned. 52%[=========> ] 69.53M 1.40MB/s eta 47s
_FOD_west_cleaned.c 52%[=========> ] 69.94M 1.38MB/s eta 47s
FOD_west_cleaned.cs 52%[=========> ] 70.37M 1.42MB/s eta 47s
OD_west_cleaned.csv 53%[=========> ] 70.78M 1.43MB/s eta 47s
D_west_cleaned.csv. 53%[=========> ] 71.17M 1.42MB/s eta 46s
_west_cleaned.csv.8 53%[=========> ] 71.65M 1.42MB/s eta 46s
west_cleaned.csv.8 54%[=========> ] 72.21M 1.45MB/s eta 46s
est_cleaned.csv.8 54%[=========> ] 72.71M 1.49MB/s eta 46s
st_cleaned.csv.8 54%[=========> ] 72.99M 1.49MB/s eta 44s
t_cleaned.csv.8 55%[==========> ] 73.40M 1.48MB/s eta 44s
_cleaned.csv.8 55%[==========> ] 73.72M 1.49MB/s eta 44s
cleaned.csv.8 55%[==========> ] 74.04M 1.48MB/s eta 44s
leaned.csv.8 56%[==========> ] 74.46M 1.52MB/s eta 43s
eaned.csv.8 56%[==========> ] 74.77M 1.49MB/s eta 43s
aned.csv.8 56%[==========> ] 75.17M 1.51MB/s eta 43s
ned.csv.8 56%[==========> ] 75.49M 1.45MB/s eta 43s
ed.csv.8 57%[==========> ] 75.99M 1.48MB/s eta 42s
d.csv.8 57%[==========> ] 76.28M 1.45MB/s eta 42s
.csv.8 57%[==========> ] 76.75M 1.45MB/s eta 42s
csv.8 58%[==========> ] 77.19M 1.44MB/s eta 42s
sv.8 58%[==========> ] 77.59M 1.40MB/s eta 41s
v.8 58%[==========> ] 78.00M 1.41MB/s eta 41s
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8 59%[==========> ] 78.75M 1.42MB/s eta 41s
59%[==========> ] 79.14M 1.45MB/s eta 39s
F 59%[==========> ] 79.53M 1.46MB/s eta 39s
FP 60%[===========> ] 79.81M 1.44MB/s eta 39s
FPA 60%[===========> ] 80.29M 1.45MB/s eta 39s
FPA_ 60%[===========> ] 80.65M 1.47MB/s eta 38s
FPA_F 61%[===========> ] 81.20M 1.53MB/s eta 38s
FPA_FO 61%[===========> ] 81.49M 1.48MB/s eta 38s
FPA_FOD 61%[===========> ] 81.87M 1.49MB/s eta 38s
FPA_FOD_ 61%[===========> ] 82.19M 1.46MB/s eta 37s
FPA_FOD_w 62%[===========> ] 82.60M 1.43MB/s eta 37s
FPA_FOD_we 62%[===========> ] 82.94M 1.42MB/s eta 37s
FPA_FOD_wes 62%[===========> ] 83.38M 1.43MB/s eta 37s
FPA_FOD_west 63%[===========> ] 83.78M 1.45MB/s eta 36s
FPA_FOD_west_ 63%[===========> ] 84.19M 1.45MB/s eta 36s
FPA_FOD_west_c 63%[===========> ] 84.69M 1.49MB/s eta 36s
FPA_FOD_west_cl 63%[===========> ] 85.00M 1.46MB/s eta 36s
FPA_FOD_west_cle 64%[===========> ] 85.31M 1.45MB/s eta 35s
FPA_FOD_west_clea 64%[===========> ] 85.69M 1.43MB/s eta 35s
FPA_FOD_west_clean 64%[===========> ] 86.09M 1.41MB/s eta 35s
FPA_FOD_west_cleane 64%[===========> ] 86.39M 1.39MB/s eta 35s
PA_FOD_west_cleaned 65%[============> ] 86.70M 1.38MB/s eta 34s
A_FOD_west_cleaned. 65%[============> ] 87.15M 1.40MB/s eta 34s
_FOD_west_cleaned.c 65%[============> ] 87.51M 1.42MB/s eta 34s
FOD_west_cleaned.cs 66%[============> ] 87.87M 1.42MB/s eta 34s
OD_west_cleaned.csv 66%[============> ] 88.27M 1.40MB/s eta 33s
D_west_cleaned.csv. 66%[============> ] 88.57M 1.36MB/s eta 33s
_west_cleaned.csv.8 66%[============> ] 89.02M 1.39MB/s eta 33s
west_cleaned.csv.8 67%[============> ] 89.44M 1.37MB/s eta 33s
est_cleaned.csv.8 67%[============> ] 89.83M 1.37MB/s eta 31s
st_cleaned.csv.8 67%[============> ] 90.21M 1.39MB/s eta 31s
t_cleaned.csv.8 68%[============> ] 90.58M 1.41MB/s eta 31s
_cleaned.csv.8 68%[============> ] 90.80M 1.35MB/s eta 31s
cleaned.csv.8 68%[============> ] 91.07M 1.33MB/s eta 31s
leaned.csv.8 68%[============> ] 91.53M 1.37MB/s eta 31s
eaned.csv.8 69%[============> ] 91.97M 1.39MB/s eta 31s
aned.csv.8 69%[============> ] 92.34M 1.39MB/s eta 31s
ned.csv.8 69%[============> ] 92.67M 1.38MB/s eta 29s
ed.csv.8 69%[============> ] 92.98M 1.36MB/s eta 29s
d.csv.8 70%[=============> ] 93.41M 1.37MB/s eta 29s
.csv.8 70%[=============> ] 93.73M 1.37MB/s eta 29s
csv.8 70%[=============> ] 94.17M 1.38MB/s eta 28s
sv.8 71%[=============> ] 94.47M 1.34MB/s eta 28s
v.8 71%[=============> ] 94.84M 1.33MB/s eta 28s
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8 71%[=============> ] 95.51M 1.33MB/s eta 27s
72%[=============> ] 95.88M 1.35MB/s eta 27s
F 72%[=============> ] 96.29M 1.37MB/s eta 27s
FP 72%[=============> ] 96.79M 1.39MB/s eta 27s
FPA 73%[=============> ] 97.13M 1.36MB/s eta 26s
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2025-08-12 10:15:26 (1.38 MB/s) - ‘FPA_FOD_west_cleaned.csv.8’ saved [139402360/139402360]
--2025-08-12 10:15:26-- http://data/
Resolving data (data)... failed: nodename nor servname provided, or not known.
wget: unable to resolve host address ‘data’
FINISHED --2025-08-12 10:15:26--
Total wall clock time: 1m 37s
Downloaded: 1 files, 133M in 1m 37s (1.38 MB/s)
data = pd.read_csv("FPA_FOD_west_cleaned.csv")
data.head()
| DISCOVERY_DOY | FIRE_YEAR | STATE | FIPS_CODE | NWCG_GENERAL_CAUSE | Annual_etr | Annual_precipitation | Annual_tempreture | pr | tmmn | ... | GHM | NDVI-1day | NPL | Popo_1km | RPL_THEMES | RPL_THEME1 | RPL_THEME2 | RPL_THEME3 | RPL_THEME4 | Distance2road | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2007 | CA | 6053.0 | Misuse of fire by a minor | 1625 | 257 | 286.0 | 0.0 | 276.500000 | ... | 0.42 | 0.00 | 1.0 | 1.1494 | 0.055 | 0.027 | 0.245 | 0.039 | 0.203 | 43.0 |
| 1 | 1 | 2007 | CA | 6019.0 | Arson/incendiarism | 1819 | 383 | 290.0 | 0.0 | 273.200012 | ... | 0.35 | 0.50 | 1.0 | 0.1652 | 0.525 | 0.719 | 0.499 | 0.302 | 0.405 | 40.2 |
| 2 | 1 | 2007 | CA | 6089.0 | Misuse of fire by a minor | 2293 | 985 | 290.0 | 0.0 | 275.100006 | ... | 0.16 | 0.42 | 1.0 | 0.0504 | 0.476 | 0.635 | 0.516 | 0.002 | 0.581 | 43.8 |
| 3 | 1 | 2007 | CA | 6089.0 | Misuse of fire by a minor | 2293 | 985 | 290.0 | 0.0 | 275.100006 | ... | 0.16 | 0.42 | 1.0 | 0.0504 | 0.476 | 0.635 | 0.516 | 0.002 | 0.581 | 43.8 |
| 4 | 1 | 2007 | CA | 6079.0 | Debris and open burning | 2423 | 102 | 289.0 | 0.0 | 271.299988 | ... | 0.18 | 0.16 | 1.0 | 0.0718 | 0.295 | 0.309 | 0.321 | 0.105 | 0.313 | 41.0 |
5 rows × 40 columns
data.columns
Index(['DISCOVERY_DOY', 'FIRE_YEAR', 'STATE', 'FIPS_CODE',
'NWCG_GENERAL_CAUSE', 'Annual_etr', 'Annual_precipitation',
'Annual_tempreture', 'pr', 'tmmn', 'vs', 'fm100', 'fm1000', 'bi', 'vpd',
'erc', 'Elevation_1km', 'Aspect_1km', 'erc_Percentile', 'Slope_1km',
'TPI_1km', 'EVC', 'Evacuation', 'SDI', 'FRG', 'No_FireStation_5.0km',
'Mang_Name', 'GAP_Sts', 'GACC_PL', 'GDP', 'GHM', 'NDVI-1day', 'NPL',
'Popo_1km', 'RPL_THEMES', 'RPL_THEME1', 'RPL_THEME2', 'RPL_THEME3',
'RPL_THEME4', 'Distance2road'],
dtype='object')
The data set includes meteorological, topological, social, and fire management variables:
‘DISCOVERY_DOY’: Day of year on which the fire was discovered or confirmed to exist
‘FIRE_YEAR’: Calendar year in which the fire was discovered or confirmed to exist
‘STATE’: Two-letter alphabetic code for the state in which the fire burned (or originated), based on fire report
‘FIPS_CODE’: Five digit code from the Federal Information Process Standards publication 6-4 for representation of counties and equivalent entities, based on the nominal designation in the fire report.
‘Annual_etr’: Annual total reference evaporatranspiration (mm)
‘Annual_temperature’: Annual average temperature (K)
‘pr’ : Precipitation amount (mm)
‘tmmn’: Minimum temperature (K)
‘vs’: Wind velocity at 10 m above ground (m/s)
‘fm100’: 100-hour dead fuel moisture (%)
‘fm1000’: 1000-hour dead fuel moisture (%)
‘bi’: Burning index (NFDRS fire danger index)
‘vpd’: Mean vapor pressure deficit (kPa)
‘erc’: Energy release component (NFDRS fire danger index)
‘Elevation_1km’: Average elevation in 1 km radius around the ignition point
‘Aspect_1km’: Average aspect in 1 km radius around the ignition point
‘erc_Percentile’: Percentile range of energy release component
‘Slope_1km’: Average slope in 1 km radius around the ignition point
‘TPI_1km’: Average Topographic Position Index in 1 km radius around the ignition point
‘EVC’: Existing Vegetation Cover - vertically projected percent cover of the live canopy layer for a specific area (%)
‘Evacuation’: Estimate ground transport time in hours from the fire ignition point to a definitive care facility (hospital)
‘SDI’: Suppression difficulty index (Rodriguez y Silva et al. 2020): relative difficulty of fire control
‘FRG’: Fire regime group - presumed historical fire regime
‘No_FireStation_5.0km’: Number of fire stations in a 5 km radius around the fire ignition point
‘Mang_Name’: The land manager or administrative agency standardized for the US
‘GAP_Sts’: GAP status code classifies management intent to conserve biodiversity
‘GACC_PL’: Geographic Area Coordination Center (GACC) Preparedness Level
‘GDP’: Annual Gross Domestic Product Per Capita
‘GHM’: Cumulative Measure of the human modification of lands within 1 km of the fire ignition point
‘NDVI-1day’: Normalized Difference Vegetation Index (NDVI) on the day prior to ignition
‘NPL’: National Preparedness Level
‘Popo_1km’: Average population density within a 1 km radius around the fire ignition point
‘RPL_THEMES’: Social Vulnerability Index (Overall Percentile Ranking)
‘RPL_THEME1’: Percentile Ranking for socioeconomic theme summary
‘RPL_THEME2’: Percentile Ranking for Household Composition theme summary
‘RPL_THEME3’: Percentile Ranking for Minority Status/Language theme
‘RPL_THEME4’: Precentile ranking for Housing Type/Transportion theme
‘Distance2road’: Distance to the nearest road
len(data)
519689
data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 519689 entries, 0 to 519688
Data columns (total 40 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 DISCOVERY_DOY 519689 non-null int64
1 FIRE_YEAR 519689 non-null int64
2 STATE 519689 non-null object
3 FIPS_CODE 519689 non-null float64
4 NWCG_GENERAL_CAUSE 519689 non-null object
5 Annual_etr 519689 non-null int64
6 Annual_precipitation 519689 non-null int64
7 Annual_tempreture 519689 non-null float64
8 pr 519689 non-null float64
9 tmmn 519689 non-null float64
10 vs 519689 non-null float64
11 fm100 519689 non-null float64
12 fm1000 519689 non-null float64
13 bi 519689 non-null float64
14 vpd 519689 non-null float64
15 erc 519689 non-null float64
16 Elevation_1km 519689 non-null float64
17 Aspect_1km 519689 non-null float64
18 erc_Percentile 519689 non-null float64
19 Slope_1km 519689 non-null float64
20 TPI_1km 519689 non-null float64
21 EVC 519689 non-null float64
22 Evacuation 519689 non-null float64
23 SDI 519689 non-null float64
24 FRG 519689 non-null int64
25 No_FireStation_5.0km 519689 non-null float64
26 Mang_Name 519689 non-null int64
27 GAP_Sts 519689 non-null float64
28 GACC_PL 519689 non-null float64
29 GDP 519689 non-null float64
30 GHM 519689 non-null float64
31 NDVI-1day 519689 non-null float64
32 NPL 519689 non-null float64
33 Popo_1km 519689 non-null float64
34 RPL_THEMES 519689 non-null float64
35 RPL_THEME1 519689 non-null float64
36 RPL_THEME2 519689 non-null float64
37 RPL_THEME3 519689 non-null float64
38 RPL_THEME4 519689 non-null float64
39 Distance2road 519689 non-null float64
dtypes: float64(32), int64(6), object(2)
memory usage: 158.6+ MB
firecauses = data['NWCG_GENERAL_CAUSE'].value_counts()
print(firecauses)
NWCG_GENERAL_CAUSE
Natural 168349
Missing data/not specified/undetermined 150427
Equipment and vehicle use 48994
Debris and open burning 40516
Recreation and ceremony 38665
Arson/incendiarism 28090
Smoking 13547
Misuse of fire by a minor 11523
Power generation/transmission/distribution 6469
Fireworks 6373
Railroad operations and maintenance 3074
Other causes 2068
Firearms and explosives use 1594
Name: count, dtype: int64
## Deal with some bad data
data.loc[data["GHM"]<0.0,"GHM"] = np.nan
data.loc[data["SDI"]<0.0,"SDI"] = np.nan
data['FRG'] = data['FRG'].replace(-9999,np.nan)
data["RPL_THEMES"] = data["RPL_THEMES"].replace(-999.0,np.nan)
data["RPL_THEME1"] = data["RPL_THEME1"].replace(-999.0,np.nan)
data["RPL_THEME2"] = data["RPL_THEME2"].replace(-999.0,np.nan)
data["RPL_THEME3"] = data["RPL_THEME3"].replace(-999.0,np.nan)
data["RPL_THEME4"] = data["RPL_THEME4"].replace(-999.0,np.nan)
import matplotlib.pyplot as plt
# extra code – the next 5 lines define the default font sizes
plt.rc('font', size=10)
plt.rc('axes', labelsize=10, titlesize=10)
plt.rc('legend', fontsize=10)
plt.rc('xtick', labelsize=10)
plt.rc('ytick', labelsize=10)
data.hist(bins=50, figsize=(12, 8))
#save_fig("attribute_histogram_plots") # extra code
plt.show()
data_cleaned = data.dropna().reset_index(drop=True)
Separate out the fires with no known cause#
First, let’s separate all of the fires where NWCG_GENERAL_CAUSE has the label Missing data/not specified/undetermined.
data_sorted = data_cleaned.iloc[np.where(data_cleaned['NWCG_GENERAL_CAUSE'] == 'Missing data/not specified/undetermined')[0].tolist() +
np.where(data_cleaned['NWCG_GENERAL_CAUSE'] != 'Missing data/not specified/undetermined')[0].tolist()].reset_index(drop=True).copy()
data_sorted
| DISCOVERY_DOY | FIRE_YEAR | STATE | FIPS_CODE | NWCG_GENERAL_CAUSE | Annual_etr | Annual_precipitation | Annual_tempreture | pr | tmmn | ... | GHM | NDVI-1day | NPL | Popo_1km | RPL_THEMES | RPL_THEME1 | RPL_THEME2 | RPL_THEME3 | RPL_THEME4 | Distance2road | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2007 | CA | 6065.0 | Missing data/not specified/undetermined | 2359 | 100 | 292.0 | 0.0 | 277.799988 | ... | 0.84 | 0.22 | 1.0 | 5.2191 | 0.261 | 0.167 | 0.424 | 0.427 | 0.256 | 38.5 |
| 1 | 1 | 2007 | CA | 6065.0 | Missing data/not specified/undetermined | 2452 | 110 | 291.0 | 0.0 | 275.899994 | ... | 0.61 | 0.17 | 1.0 | 1.3687 | 0.927 | 0.969 | 0.940 | 0.846 | 0.607 | 38.3 |
| 2 | 1 | 2007 | AZ | 0.0 | Missing data/not specified/undetermined | 3146 | 135 | 292.0 | 0.0 | 273.100006 | ... | 0.04 | 0.11 | 1.0 | 0.0000 | 0.504 | 0.829 | 0.535 | 0.046 | 0.394 | 36.2 |
| 3 | 1 | 2007 | CA | 6065.0 | Missing data/not specified/undetermined | 3546 | 20 | 297.0 | 0.0 | 277.100006 | ... | 0.92 | 0.04 | 1.0 | 8.1135 | 0.611 | 0.498 | 0.653 | 0.594 | 0.688 | 37.5 |
| 4 | 1 | 2007 | CA | 6065.0 | Missing data/not specified/undetermined | 2486 | 92 | 292.0 | 0.0 | 277.799988 | ... | 0.88 | 0.18 | 1.0 | 13.7651 | 0.939 | 0.833 | 0.879 | 0.822 | 0.875 | 38.8 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 518654 | 364 | 2003 | CO | 0.0 | Arson/incendiarism | 2222 | 390 | 284.0 | 0.0 | 263.500000 | ... | 0.08 | 0.29 | 1.0 | 0.0007 | 0.464 | 0.560 | 0.089 | 0.688 | 0.695 | 16.8 |
| 518655 | 364 | 2003 | CO | 8043.0 | Arson/incendiarism | 1891 | 636 | 280.0 | 0.0 | 261.700012 | ... | 0.06 | 0.37 | 1.0 | 0.0003 | 0.464 | 0.560 | 0.089 | 0.688 | 0.695 | 16.8 |
| 518656 | 365 | 2003 | CA | 6025.0 | Recreation and ceremony | 2846 | 63 | 298.0 | 0.0 | 286.500000 | ... | 0.19 | -0.00 | 1.0 | 0.0000 | 0.715 | 0.914 | 0.545 | 0.500 | 0.421 | 8.7 |
| 518657 | 365 | 2003 | CA | 0.0 | Debris and open burning | 1805 | 994 | 287.0 | 0.0 | 274.100006 | ... | 0.39 | 0.02 | 1.0 | 0.4738 | 0.216 | 0.509 | 0.207 | 0.008 | 0.151 | 33.6 |
| 518658 | 365 | 2003 | CA | 6065.0 | Equipment and vehicle use | 2048 | 318 | 292.0 | 0.0 | 280.399994 | ... | 0.81 | 0.11 | 1.0 | 1.1717 | 0.636 | 0.785 | 0.567 | 0.453 | 0.377 | 6.1 |
518659 rows × 40 columns
data_unknown = data_sorted.loc[data_sorted["NWCG_GENERAL_CAUSE"] == "Missing data/not specified/undetermined"].reset_index(drop=True).copy()
data_known = data_sorted.loc[data_sorted["NWCG_GENERAL_CAUSE"] != "Missing data/not specified/undetermined"].reset_index(drop=True).copy()
data_known["NWCG_GENERAL_CAUSE"].value_counts()
NWCG_GENERAL_CAUSE
Natural 168126
Equipment and vehicle use 48895
Debris and open burning 40450
Recreation and ceremony 38498
Arson/incendiarism 28035
Smoking 13510
Misuse of fire by a minor 11508
Power generation/transmission/distribution 6453
Fireworks 6348
Railroad operations and maintenance 3062
Other causes 2064
Firearms and explosives use 1584
Name: count, dtype: int64
Since only the first class is due to natural causes (typically ignition is due to lightning), and all the other categories are related to human activity, we can also label fires as being “natural” or “anthropogenic”. We’ll create a binary variable called “IsNatural” which has a value of 1 (True) if it is fire caused by natural causes or 0 (False) if it is a fire caused by any of the other causes related to human activity.
data_known["IsNatural"] = (data_known["NWCG_GENERAL_CAUSE"] == "Natural").astype(int)
data_known["IsNatural"].value_counts()
IsNatural
0 200407
1 168126
Name: count, dtype: int64
Data Pre-Processing#
For decision trees and random forests, we generally don’t have to worry as much about scaling (compared with models like neural networks), since they work based on finding threshold values in the data sets.
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.compose import ColumnTransformer
causes = data_known[['NWCG_GENERAL_CAUSE']]
isnatural = data_known[["IsNatural"]]
features = data_known.copy().drop(["NWCG_GENERAL_CAUSE","IsNatural"],axis=1)
features_unknown = data_unknown.copy().drop(["NWCG_GENERAL_CAUSE"],axis=1)
We’ll create two labels for our data set. The first is a binary label, for whether the fire was caused by natural or anthropogenic causes.
y_binary = isnatural.to_numpy()
classnames_binary = ["Anthropogenic","Natural"]
The second set of labels will be multi-class, and include all of the possible causes for the fires included in the NWCG_GENERAL_CAUSE column.
ordenc = OrdinalEncoder()
y_multiclass = ordenc.fit_transform(causes)
classnames_multi = ordenc.categories_[0]
print(classnames_multi)
['Arson/incendiarism' 'Debris and open burning'
'Equipment and vehicle use' 'Firearms and explosives use' 'Fireworks'
'Misuse of fire by a minor' 'Natural' 'Other causes'
'Power generation/transmission/distribution'
'Railroad operations and maintenance' 'Recreation and ceremony' 'Smoking']
Now we will create the pipeline to transform the variables in the features dataframe as input to the model.
categorical_cols = ["STATE"]
numerical_cols = ['DISCOVERY_DOY', 'FIRE_YEAR', 'FIPS_CODE', 'Annual_etr', 'Annual_precipitation','Annual_tempreture',
'pr', 'tmmn', 'vs', 'fm100', 'fm1000', 'bi', 'vpd', 'erc', 'Elevation_1km', 'Aspect_1km', 'erc_Percentile',
'Slope_1km','TPI_1km', 'EVC', 'Evacuation', 'SDI', 'FRG', 'No_FireStation_5.0km','Mang_Name', 'GAP_Sts',
'GACC_PL', 'GDP', 'GHM', 'NDVI-1day', 'NPL','Popo_1km', 'RPL_THEMES', 'RPL_THEME1', 'RPL_THEME2', 'RPL_THEME3',
'RPL_THEME4', 'Distance2road']
cat_pipeline = make_pipeline(OrdinalEncoder(),StandardScaler())
num_pipeline = make_pipeline(StandardScaler())
preprocessor = ColumnTransformer([
("n",num_pipeline,numerical_cols),
("c",cat_pipeline,categorical_cols)])
X_known = preprocessor.fit_transform(features)
We’ll use the same pipeline to transform the features associated with the unknown fires. In this case we will use transform rather than fit_transform. The difference is that the scalings and transformations will be based on the data in features (rather than features_unknown) so we will end up performing exactly the same scalings and transformations on both data sets. This is important because the models that we will train later will depend on these scalings and transformations being consistent across both data sets.
X_unknown = preprocessor.transform(features_unknown)
print(X_known.shape,X_unknown.shape)
(368533, 39) (150126, 39)
featurenames = preprocessor.get_feature_names_out()
print(featurenames)
['n__DISCOVERY_DOY' 'n__FIRE_YEAR' 'n__FIPS_CODE' 'n__Annual_etr'
'n__Annual_precipitation' 'n__Annual_tempreture' 'n__pr' 'n__tmmn'
'n__vs' 'n__fm100' 'n__fm1000' 'n__bi' 'n__vpd' 'n__erc'
'n__Elevation_1km' 'n__Aspect_1km' 'n__erc_Percentile' 'n__Slope_1km'
'n__TPI_1km' 'n__EVC' 'n__Evacuation' 'n__SDI' 'n__FRG'
'n__No_FireStation_5.0km' 'n__Mang_Name' 'n__GAP_Sts' 'n__GACC_PL'
'n__GDP' 'n__GHM' 'n__NDVI-1day' 'n__NPL' 'n__Popo_1km' 'n__RPL_THEMES'
'n__RPL_THEME1' 'n__RPL_THEME2' 'n__RPL_THEME3' 'n__RPL_THEME4'
'n__Distance2road' 'c__STATE']
Training, validation, and test split#
Then we will split the data where the cause of the fire is known into training, validation, and test data sets.
from sklearn.model_selection import train_test_split
We’ll create an index z as input to the train_test_split function. This way, we can select either the binary or multiclass labels for our training, validation, and test data sets.
z_known = np.arange(0,X_known.shape[0])
X_train, X_val_test, z_train, z_val_test = train_test_split(X_known,z_known,test_size = 0.2, random_state = 42)
X_val, X_test, z_val, z_test = train_test_split(X_val_test, z_val_test ,test_size = 0.5, random_state = 42)
z_train.shape
(294826,)
y_multiclass_train = y_multiclass[z_train].ravel()
y_multiclass_test = y_multiclass[z_test].ravel()
y_multiclass_val = y_multiclass[z_val].ravel()
y_binary_train = y_binary[z_train].ravel()
y_binary_test = y_binary[z_test].ravel()
y_binary_val = y_binary[z_val].ravel()
print(X_train.shape,X_val.shape,X_test.shape)
print(y_binary_train.shape,y_binary_val.shape,y_binary_test.shape)
(294826, 39) (36853, 39) (36854, 39)
(294826,) (36853,) (36854,)
Train logistic regression (natural vs. human causes)#
from sklearn.linear_model import LogisticRegression
log_reg = LogisticRegression(solver="lbfgs", random_state=42)
log_reg.fit(X_train, y_binary_train)
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LogisticRegression(random_state=42)
log_reg.score(X_train,y_binary_train)
0.8811231031184495
log_reg.score(X_val,y_binary_val)
0.882479038341519
y_train_predicted = log_reg.predict(X_train)
y_val_predicted = log_reg.predict(X_val)
from sklearn.metrics import confusion_matrix
from sklearn.metrics import ConfusionMatrixDisplay
confusion_matrix(y_binary_val, y_val_predicted)
array([[17695, 2302],
[ 2029, 14827]])
confusion_matrix(y_binary_train, y_train_predicted)
array([[142077, 18472],
[ 16576, 117701]])
ConfusionMatrixDisplay.from_predictions(y_binary_train, y_train_predicted,normalize='true',display_labels=classnames_binary)
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x3411c0bb0>
ConfusionMatrixDisplay.from_predictions(y_binary_val, y_val_predicted,normalize='true',display_labels=classnames_binary)
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x3418868b0>
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
Accuracy is defined as
\(\frac{TP+TN}{TP+TN+FP+FN}\)
where
TP = True positive
TN = True negative
FP = False positive
FN = False negative
When accuracy = 1.0, this indicates a perfect classifier, while 0.0 indicates no skill. However, accuracy can be missleading if our classes are imbalanced.
accuracy_score(y_binary_val,y_val_predicted)
0.882479038341519
Precision tells us how accurately the classifier is able to identify objects of a specific class. It is defined as
\( precision = \frac{TP}{TP + FP}\).
High precision means that we will tolerate false negatives, but have as few false positives as possible.
precision_score(y_binary_val, y_val_predicted)
0.8656080331601378
Recall tells us how many of the objects of a class are correctly identified. It is defined as
\(recall = \frac{TP}{TP+FN}\)
High recall means that we will tolerate false positives, but try to have as few false negatives as possible.
recall_score(y_binary_val, y_val_predicted)
0.8796274323682961
Finally, if we want to find a balance between precision and recall, we can evaluate the F1 score:
\(F_{1} = \frac{2}{recall^{-1}+precision^{-1}}\)
f1_score(y_binary_val, y_val_predicted)
0.8725614241577166
ROC curve#
The Reciever Operator Characteristic (ROC) curve can be used to evaluate the performance of a binary classifier. Because there is a trade-off between true positives and false positives depending on where we set the threshold for identifying the two classes, the ROC curve can visualize this trade-off. A classifier with no skill would line on the diagnol dashed line, and a perfect classifier would have a curve reaching the top-left corner of the plot.
from sklearn.metrics import RocCurveDisplay
svc_disp = RocCurveDisplay.from_estimator(log_reg, X_val, y_binary_val)
plt.plot(np.arange(0,1.1,0.1),np.arange(0,1.1,0.1),linestyle='--')
plt.show()
Importance of different features for logistic regression#
coefficients = log_reg.coef_
coefficients.shape
(1, 39)
x = plt.bar(featurenames,coefficients[0,:])
plt.ylabel("Coefficient values")
plt.xlabel("Feature")
plt.xticks(rotation=90)
plt.show()
Train a decision tree classifier#
from sklearn.tree import DecisionTreeClassifier
tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42)
tree_clf.fit(X_train, y_binary_train)
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DecisionTreeClassifier(max_depth=2, random_state=42)
!pip install graphviz
Requirement already satisfied: graphviz in /opt/anaconda3/envs/ML4Climate2025/lib/python3.8/site-packages (0.20.3)
We can directly visualize the decision tree using the graphviz library, and look at what thresholds it is using at each node.
from graphviz import Source
from sklearn.tree import export_graphviz
export_graphviz(
tree_clf,
out_file="decision_tree.dot",
feature_names=featurenames,
class_names=classnames_binary,
rounded=True,
filled=True
)
# Read the dot file
with open("decision_tree.dot") as f:
dot_graph = f.read()
# Adjust dpi for scaling
dot_graph = 'digraph Tree {\ndpi=50;\n' + dot_graph.split('\n', 1)[1]
Source(dot_graph)
Let’s train decision trees with greater max_depth and see how they perform on the validation data set.
depths = [2,10,20,50]
trained_decisiontrees = []
for i in depths:
tree_clf = DecisionTreeClassifier(max_depth=i, random_state=42)
trained_decisiontrees.append(tree_clf.fit(X_train, y_binary_train))
y_val_predicted = trained_decisiontrees[0].predict(X_val)
y_val_predicted
array([1, 0, 1, ..., 1, 1, 0])
ConfusionMatrixDisplay.from_estimator(trained_decisiontrees[0],X_val,y_binary_val,normalize='true',display_labels=classnames_binary)
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x341aef4f0>
ConfusionMatrixDisplay.from_estimator(trained_decisiontrees[1],X_val,y_binary_val,normalize='true',display_labels=classnames_binary)
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x341a52040>
ConfusionMatrixDisplay.from_estimator(trained_decisiontrees[2],X_val,y_binary_val,normalize='true',display_labels=classnames_binary)
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x17fd129a0>
ConfusionMatrixDisplay.from_estimator(trained_decisiontrees[3],X_val,y_binary_val,normalize='true',display_labels=classnames_binary)
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x17fd04130>
We can compare the performance of a the trained decision tree to logistic regression.
ax = plt.gca()
svc_disp = RocCurveDisplay.from_estimator(log_reg, X_val, y_binary_val,ax=ax)
svc_disp = RocCurveDisplay.from_estimator(trained_decisiontrees[1], X_val, y_binary_val,ax=ax)
ax.plot(np.arange(0,1.1,0.1),np.arange(0,1.1,0.1),linestyle='--')
plt.show()
Train a random forest classifier#
A random forest is an ensemble of decision trees. Each decision tree is grown on a different sub-sample of the data set, and their ensemble vote is typically better than that of a single decision tree. They are quite powerful methods that are still used widely in environmental science and climate research, and are particularly good on tabular data sets. They can however be rather slow to train if the training data set is large.
from sklearn.ensemble import RandomForestClassifier
rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)
rnd_clf.fit(X_train,y_binary_train)
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RandomForestClassifier(random_state=42)
ConfusionMatrixDisplay.from_estimator(rnd_clf,X_val,y_binary_val,normalize='true',display_labels=classnames_binary)
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x17fb44280>
We can compare the trained random forest with the decision tree and logistic regression. In this case the random forest does give us some improvement.
ax = plt.gca()
svc_disp = RocCurveDisplay.from_estimator(log_reg, X_val, y_binary_val,ax=ax)
svc_disp = RocCurveDisplay.from_estimator(trained_decisiontrees[1], X_val, y_binary_val,ax=ax)
svc_disp = RocCurveDisplay.from_estimator(rnd_clf, X_val, y_binary_val,ax=ax)
ax.plot(np.arange(0,1.1,0.1),np.arange(0,1.1,0.1),linestyle='--')
plt.show()
Feature importance#
With random forests, we can also get some ideas of which features are the most important for our classifier.
rnd_clf.feature_importances_
array([0.03678424, 0.010523 , 0.00857334, 0.01493381, 0.01567114,
0.02743097, 0.07660563, 0.04684962, 0.01184543, 0.01285308,
0.01466824, 0.0617133 , 0.02268836, 0.01251963, 0.09073005,
0.01565229, 0.00856963, 0.01815727, 0.01130637, 0.02685617,
0.0824383 , 0.02938211, 0.00523526, 0.02220977, 0.0093963 ,
0.02086468, 0.00383213, 0.01152713, 0.11698223, 0.01991252,
0.02078227, 0.03123305, 0.01249902, 0.01197704, 0.01121416,
0.01153355, 0.01094183, 0.0116142 , 0.01149288])
x = plt.bar(featurenames,rnd_clf.feature_importances_)
plt.ylabel("Feature Importance")
plt.xlabel("Feature")
plt.xticks(rotation=90)
plt.show()
Multiclass classification with the Random Forest#
rnd_multiclass_clf = RandomForestClassifier(n_estimators=30, random_state=42, class_weight = "balanced")
import time
start = time.time()
rnd_multiclass_clf.fit(X_train,y_multiclass_train)
end = time.time()
print(end - start)
31.419840097427368
# Print the depth of each tree
for i, tree in enumerate(rnd_multiclass_clf.estimators_):
print(f"Tree {i+1}: Depth = {tree.get_depth()}")
Tree 1: Depth = 45
Tree 2: Depth = 46
Tree 3: Depth = 49
Tree 4: Depth = 43
Tree 5: Depth = 50
Tree 6: Depth = 50
Tree 7: Depth = 48
Tree 8: Depth = 48
Tree 9: Depth = 44
Tree 10: Depth = 59
Tree 11: Depth = 50
Tree 12: Depth = 46
Tree 13: Depth = 51
Tree 14: Depth = 51
Tree 15: Depth = 43
Tree 16: Depth = 48
Tree 17: Depth = 47
Tree 18: Depth = 47
Tree 19: Depth = 46
Tree 20: Depth = 51
Tree 21: Depth = 50
Tree 22: Depth = 43
Tree 23: Depth = 44
Tree 24: Depth = 47
Tree 25: Depth = 48
Tree 26: Depth = 50
Tree 27: Depth = 48
Tree 28: Depth = 47
Tree 29: Depth = 48
Tree 30: Depth = 47
This can be slow. If we want to train a model and save the trained weights, we can use pickle so we don’t need to train this again.
import pickle
filename = 'rnd_multiclass_clf.pkl'
with open(filename, 'wb') as file:
pickle.dump(rnd_multiclass_clf, file)
Then we can load the weights in later using the following lines.
loaded_model = pickle.load(open(filename, 'rb'))
We can evaluate the trained multi-class classifier.
cmp = ConfusionMatrixDisplay.from_estimator(rnd_multiclass_clf,X_val,y_multiclass_val,normalize='true',
display_labels=classnames_multi, xticks_rotation="vertical",include_values=False);
from sklearn.metrics import classification_report
y_val_predicted = rnd_multiclass_clf.predict(X_val)
print(classification_report(y_val_predicted,y_multiclass_val,target_names = classnames_multi))
precision recall f1-score support
Arson/incendiarism 0.43 0.53 0.47 2214
Debris and open burning 0.57 0.51 0.54 4606
Equipment and vehicle use 0.62 0.51 0.56 6016
Firearms and explosives use 0.46 0.96 0.62 72
Fireworks 0.36 0.59 0.45 389
Misuse of fire by a minor 0.10 0.34 0.16 354
Natural 0.96 0.80 0.87 20021
Other causes 0.00 0.08 0.01 13
Power generation/transmission/distribution 0.05 0.49 0.10 67
Railroad operations and maintenance 0.11 0.69 0.18 49
Recreation and ceremony 0.42 0.59 0.49 2681
Smoking 0.10 0.36 0.16 371
accuracy 0.68 36853
macro avg 0.35 0.54 0.38 36853
weighted avg 0.76 0.68 0.71 36853
The classes are pretty imbalanced, so one approach we can try is over-sampling the classes that are not well-represented.
!pip install imbalanced-learn
Requirement already satisfied: imbalanced-learn in /opt/anaconda3/envs/ML4Climate2025/lib/python3.8/site-packages (0.12.4)
Requirement already satisfied: numpy>=1.17.3 in /opt/anaconda3/envs/ML4Climate2025/lib/python3.8/site-packages (from imbalanced-learn) (1.24.3)
Requirement already satisfied: scipy>=1.5.0 in /opt/anaconda3/envs/ML4Climate2025/lib/python3.8/site-packages (from imbalanced-learn) (1.10.1)
Requirement already satisfied: scikit-learn>=1.0.2 in /opt/anaconda3/envs/ML4Climate2025/lib/python3.8/site-packages (from imbalanced-learn) (1.3.2)
Requirement already satisfied: joblib>=1.1.1 in /opt/anaconda3/envs/ML4Climate2025/lib/python3.8/site-packages (from imbalanced-learn) (1.4.2)
Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/anaconda3/envs/ML4Climate2025/lib/python3.8/site-packages (from imbalanced-learn) (3.5.0)
from imblearn.over_sampling import SMOTE
The SMOTE algorithm interpolates between the points in each class in order to create new examples similar to the training data set in order to augment the data set.
sm = SMOTE(random_state=42)
X_resampled, y_resampled = sm.fit_resample(X_train,y_multiclass_train)
This makes our data set much larger however.
X_resampled.shape
(1611324, 39)
X_resampled.shape[0]/X_train.shape[0]
5.465338877846595
We will randomly sample the resampled data set so that we will have the same size as the original training data set.
z = np.arange(0,X_resampled.shape[0])
idx = np.random.choice(z, size=X_train.shape[0], replace=False)
X_balanced = X_resampled[idx]
y_balanced = y_resampled[idx]
rnd_multiclass_clf2 = RandomForestClassifier(n_estimators=30, random_state=42, class_weight = "balanced")
start = time.time()
rnd_multiclass_clf2.fit(X_balanced,y_balanced)
end = time.time()
print(end - start)
50.93929100036621
cmp = ConfusionMatrixDisplay.from_estimator(rnd_multiclass_clf2,X_val,y_multiclass_val,normalize='true',
display_labels=classnames_multi, xticks_rotation="vertical",include_values=False);
y_val_predicted_oversampled = rnd_multiclass_clf2.predict(X_val)
print(classification_report(y_val_predicted_oversampled,y_multiclass_val,target_names = classnames_multi))
precision recall f1-score support
Arson/incendiarism 0.42 0.47 0.44 2451
Debris and open burning 0.50 0.53 0.51 3835
Equipment and vehicle use 0.47 0.53 0.50 4360
Firearms and explosives use 0.67 0.33 0.44 306
Fireworks 0.55 0.34 0.42 1029
Misuse of fire by a minor 0.24 0.21 0.23 1315
Natural 0.86 0.89 0.88 16266
Other causes 0.15 0.07 0.09 445
Power generation/transmission/distribution 0.28 0.17 0.21 1013
Railroad operations and maintenance 0.32 0.19 0.24 537
Recreation and ceremony 0.53 0.50 0.51 4076
Smoking 0.21 0.23 0.22 1220
accuracy 0.63 36853
macro avg 0.43 0.37 0.39 36853
weighted avg 0.63 0.63 0.63 36853
x = plt.bar(featurenames,rnd_multiclass_clf2.feature_importances_)
plt.ylabel("Feature Importance")
plt.xlabel("Feature")
plt.xticks(rotation=90)
plt.show()
