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.

In [1]:

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.

In [3]:

from sklearn.linear_model import LogisticRegression

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

Out[3]:

LogisticRegression()

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Make Predictions¶

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

In [4]:

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.

In [5]:

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

Accuracy on training set: 1.0

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