Coursera

Ungraded Lab: Logistic Regression using Scikit-Learn

Goals

In this lab you will:

• Train a logistic regression model using scikit-learn.

Dataset

Let’s start with the same dataset as before.

import numpy as np

X = np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])
y = np.array([0, 0, 0, 1, 1, 1])

Fit the model

The code below imports the logistic regression model from scikit-learn. You can fit this model on the training data by calling fit function.

from sklearn.linear_model import LogisticRegression

lr_model = LogisticRegression()
lr_model.fit(X, y)

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False)

Make Predictions

You can see the predictions made by this model by calling the predict function.

y_pred = lr_model.predict(X)

print("Prediction on training set:", y_pred)

Prediction on training set: [0 0 0 1 1 1]

Calculate accuracy

You can calculate this accuracy of this model by calling the score function.

print("Accuracy on training set:", lr_model.score(X, y))

Accuracy on training set: 1.0