How to calibrate a classification model?

How to calibrate a classification model?

How to calibrate a classification model?

This recipe helps you calibrate a classification model


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, ytrain)

Step-4 We will calibrate the model.

# calibrate model on validation data calibrator = CalibratedClassifierCV(clf_logreg, cv='prefit'),ytest)

Step-5 We will make predictions.

ypred = calibrator.predict(Xtest)

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