How to calibrate a classification model in ML
This recipe helps you calibrate a classification model in ML
Recipe Objective
How to calibrate a classification model ?
Step-1 Imporing Libraries.
from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict
import pandas as pd
import numpy as np
import seaborn as sns
from sklearn.calibration import calibration_curve, CalibratedClassifierCV
from sklearn.linear_model import LogisticRegression
Step 2- Importing dataset
We will import the dataset through seaborn library and preparing it to apply ML model.
iris = sns.load_dataset('iris')
X=iris.drop(columns='species')
y=iris['species']
Xtrain, Xtest, ytrain, ytest= train_test_split(X,y, test_size=0.3, random_state=20)
Step 3- We will apply ML model.
# Logistic Regression
clf_logreg = LogisticRegression()
# fit model
clf_logreg.fit(Xtrain, ytrain)
Step-4 We will calibrate the model.
# calibrate model on validation data
calibrator = CalibratedClassifierCV(clf_logreg, cv='prefit')
calibrator.fit(Xtest,ytest)
Step-5 We will make predictions.
ypred = calibrator.predict(Xtest)
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