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This tutorial explains how to calculate r^2 from scikit-learn on a regression model from catboost.
During this tutorial you will build and evaluate a model to predict arrival delay for flights in and out of NYC in 2013.
This tutorial uses:
Open a new Jupyter notebook and import the following:
import statsmodels.api as sm
import pandas as pd
import numpy as np
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
from catboost import CatBoostRegressor, Pool
The data is from rdatasets imported using the Python package statsmodels.
df = sm.datasets.get_rdataset('flights', 'nycflights13').data
df.info()
RangeIndex: 336776 entries, 0 to 336775
Data columns (total 19 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 year 336776 non-null int64
1 month 336776 non-null int64
2 day 336776 non-null int64
3 dep_time 328521 non-null float64
4 sched_dep_time 336776 non-null int64
5 dep_delay 328521 non-null float64
6 arr_time 328063 non-null float64
7 sched_arr_time 336776 non-null int64
8 arr_delay 327346 non-null float64
9 carrier 336776 non-null object
10 flight 336776 non-null int64
11 tailnum 334264 non-null object
12 origin 336776 non-null object
13 dest 336776 non-null object
14 air_time 327346 non-null float64
15 distance 336776 non-null int64
16 hour 336776 non-null int64
17 minute 336776 non-null int64
18 time_hour 336776 non-null object
dtypes: float64(5), int64(9), object(5)
memory usage: 48.8+ MB
df.isnull().sum()
year 0
month 0
day 0
dep_time 8255
sched_dep_time 0
dep_delay 8255
arr_time 8713
sched_arr_time 0
arr_delay 9430
carrier 0
flight 0
tailnum 2512
origin 0
dest 0
air_time 9430
distance 0
hour 0
minute 0
time_hour 0
dtype: int64
As this model will predict arrival delay, the Null values are caused by flights did were cancelled or diverted. These can be excluded from this analysis.
df.dropna(inplace=True)
df['arr_hour'] = df.arr_time.apply(lambda x: int(np.floor(x/100)))
df['arr_minute'] = df.arr_time.apply(lambda x: int(x - np.floor(x/100)*100))
df['sched_arr_hour'] = df.sched_arr_time.apply(lambda x: int(np.floor(x/100)))
df['sched_arr_minute'] = df.sched_arr_time.apply(lambda x: int(x - np.floor(x/100)*100))
df['sched_dep_hour'] = df.sched_dep_time.apply(lambda x: int(np.floor(x/100)))
df['sched_dep_minute'] = df.sched_dep_time.apply(lambda x: int(x - np.floor(x/100)*100))
df.rename(columns={'hour': 'dep_hour',
'minute': 'dep_minute'}, inplace=True)
target = 'arr_delay'
y = df[target]
X = df.drop(columns=[target, 'flight', 'tailnum', 'time_hour', 'year', 'dep_time', 'sched_dep_time', 'arr_time', 'sched_arr_time', 'dep_delay'])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=1066)
Build the regression model
categorical_features = X_train.select_dtypes(exclude=[np.number])
train_pool = Pool(X_train, y_train, categorical_features)
test_pool = Pool(X_test, y_test, categorical_features)
model = CatBoostRegressor(iterations=500, max_depth=5, learning_rate=0.05, random_seed=1066, logging_level='Silent')
model.fit(X_train, y_train, eval_set=test_pool, cat_features=categorical_features, use_best_model=True, early_stopping_rounds=10)
Using r2_score from scikit-learn, calculate the r^2.
r2 = r2_score(y_test, model.predict(X_test))
r2
0.9418282555971598
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