Logistic Regression using Scikit-Learn

Logistic Regression using Scikit-Learn

will show you how to use the popular sci-kit learn library to train a logistic regression model for classification.

After you run gradient descent in the lab, there will be a nice set of animated plots that show gradient descent in action. You’ll see the sigmoid function, the contour plot of the cost, the 3D surface plot of the cost, and the learning curve all evolve as gradient descent runs

This Logistic Regression using Scikit-Learn is part of DeepLearning.AI course: Machine Learning Specialization / Course 1: Supervised Machine Learning: Regression and Classification In this course we will learn the difference between supervised and unsupervised learning and regression and classification tasks. Develop a linear regression model. Understand and implement the purpose of a cost function. Understand and implement gradient descent as a machine learning training method.

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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()

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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