How to Use the Sklearn Logistic Regression Function

In this tutorial, I’ll show you how to use the Sklearn Logistic Regression function to create logistic regression models in Python.

I’ll quickly review what logistic regression is, explain the syntax of Sklearn LogisticRegression, and I’ll show you a step-by-step example of how to use the technique.

If you need something specific, just click on any of the following links. The link will take you to the appropriate section in the tutorial.

Table of Contents:

A Quick Introduction to Logistic Regression

To understand what the Sklearn logistic regression function does, you should probably have a basic understanding of what logistic regression is, generally.

That being said, let’s quickly review what logistic regression is, and how it works.

Linear Regression is a type of Machine Learning Technique

Logistic regression is a type of machine learning technique.

Specifically, logistic regression is a type of classification algorithm.

Remember: classification is one of the primary “tasks” of machine learning.

A simple image that shows the difference between regression vs classification.

In classification, we input a dataset and classify each row of data by adding a new “label”. So we can use classification to do things like:

  • identify cars vs bikes (machine vision)
  • identify spam vs not-spam
  • predict pass vs fail

So in classification, the model produces a categorical prediction as an output. This is in contrast to regression, where the output is a real-valued number.

Logistic Regression is Type of Classification Technique

Logistic regression is a specific type of classification algorithm.

There are many algorithms and techniques that can solve classification tasks, like decision trees, support vector machines, and K-Nearest Neighbor classification. We can even use deep learning (i.e., neural networks) to perform classification.

But arguably, the simplest classification technique, and probably one of the most common, is logistic regression.

In Logistic Regression, We Fit an S-Shaped Curve to the Data

Logistic regression solves classification problems in a particular way.

Essentially, in logistic regression we fit an s-shaped curve to the training data.

A simple image that shows how we fit an S-shaped curve to the binary training data, when we perform logistic regression.

Specifically, we fit a function to the training data of the form:

(1)   \begin{equation*} P(X) = \frac{1}{1 + e^{-\beta_0 - \beta_1X_1}} \end{equation*}

The equation above is for a model with one X variable (feature), but it generalizes to multiple features.

So the function takes a real-valued input (X), but outputs a number between 0 and 1.

We can interpret the output of this function as a probability, and then produce an output prediction \hat{y} as follows:

(2)   \begin{equation*}   \hat{y} = \begin{cases}     1, & \text{if $P(x) > .5$}\\     0, & \text{if $P(x) \leq .5$}   \end{cases} \end{equation*}

So essentially, when we use logistic regression:

  • we fit an s-shaped curve to the training data
  • the s-shaped curve is a function of the input features
  • the s-shaped curve outputs a “probability” between 0 and 1
  • we classify the observation with a label/category, based on the computed probability

Sklearn LogisticRegression Builds Logistic Regression Models in Python

Now, let’s return to Scikit Learn.

The Sklearn LogisticRegression function builds logistic regression models in Python.

Using this function, we can train logistic regression models, “score” the accuracy of the model, and make “predictions”.

To do this though, you need to know the syntax.

The Syntax of the Sklearn Logistic Regression Method

Now that we’ve reviewed what the Sklearn Logistic Regression method does, let’s take a look at the syntax.

LogisticRegression Steps

Using the Scikit Learn LogisticRegression function to create a logistic regression model typically involves several steps:

  • initialize the model
  • train the model with the training data
  • make predictions

To be clear, I’m simplifying things slightly. The process for creating a machine learning model is often a little more complicated than this.

However, at a high level, the above steps are what you need to do when you build and use a logistic regression model. This is important, because the syntax that we use reflects those steps.

Initialize Sklearn LogisticRegression

When you build a logistic regression model in Python with Scikit Learn, the first step is to initialize the model.

Before we initialize the model, we first need to import the function from Scikit learn:

from sklearn.linear_model import LogisticRegression

After you import the function, you simply call it as LogisticRegression().

An image that shows how to initialize a new instance of Sklearn LogisticRegression in Python.

Inside the parenthesis, there are a few optional parameters that you can use to control how the function behaves. I’ll explain those in a section below.

When we call the LogisticRegression function, we commonly save the model object with a name. Just like when you create a list, or integer, or other Python object and store it with a variable name, you can also store a LogisticRegression object with a variable name.

In the above syntax example, I’ve stored the LogisticRegression object with the name my_logistic_reg. You can use any variable name to store the object, as long as it conforms to Pythons variable naming rules.

Fit and Train Syntax

Once we initialize the LogisticRegression object, we can train the model and also use the model to make predictions:

An image that shows the syntax for how to initialize, train, and predict with the Sklearn Logistic Regression function.

You can learn more about training models and predicting with them in our tutorials about Sklearn Fit and Sklearn Predict.

Sklearn LogisticRegression Parameters

The Sklearn Logistic regression function has several optional parameters:

  • penalty
  • dual
  • tol
  • C
  • fit_intercept
  • intercept_scaling
  • class_weight
  • random_state
  • solver
  • max_iter
  • multi_class
  • verbose
  • warm_start
  • n_jobs
  • l1_ratio

These are somewhat uncommonly used for beginners so we’re not going to discuss them here in detail.

(But, if you have a specific question about on of the Sklearn Logistic Regression parameters, leave your question in the comments section below. I may be able to answer your question.)

Example: How to Build a Logistic Regression Model in Python

Now that we’ve looked at the syntax for Sklearn LogisticRegression, let’s look at an example of how to build a logistic regression model in Python.

Here, I’ll show you a clear example which will involve several steps.


Import Packages and Functions

Before you run the example, you’ll need to import a couple of Python packages and you’ll need to import some tools from Scikit Learn.

Specifically, we’ll import Numpy and Seaborn. We’ll use Numpy to create and wrangle a numeric dataset that we can work with. And we’ll use Seaborn to plot the data.

We also need to import some tools from Scikit learn.

We need to import train_test_split to split our dataset into training data and test data.

import numpy as np
import seaborn as sns

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

Create Data

Now that we have our packages and functions loaded, let’s create a dataset that we can work with.

Here, we’re going to create a dataset with an x variable and a y variable.

The x variable will have values between -10 and 10.

The y variable will be a binary variable with values 0 or 1.

We’ll structure it so that values of x_var that are greater than 0 are likely to have a y value of 1. But, there will be a little noise in the data such that a few values where x_var is less than 0 will have a y value of 1 and visa versa.

observation_count = 201
x_var = np.linspace(start = -10
                    ,stop = 10
                    ,num = observation_count

noise_var = np.random.normal(size = observation_count
                             ,loc = 0
                             ,scale = 3

y_var = np.where(x_var + noise_var >= 0 , 1, 0)

Notice that we’re using Numpy linspace to create x_var.

We’re creating y_var using Numpy where, so that y_var equals 0 when x_var plus noise_var are greater than or equal to 0. The data in noise_var just just random normal “noise”, created with Numpy random normal and Numpy random seed.

Plot Data

Before we move on, let’s quickly plot the data using the Seaborn scatterplot function.

sns.scatterplot(x = x_var
                ,y = y_var
                ,hue = y_var


A scatterplot of binary data.  We will be able to use scikit learn to train a logistic regression model on this data using sklearn LogisticRegression.

You’ll notice that:

  • most of the values for x_var > 0 have a y_var value of 1
  • most of the values for x_var < 0 have a y_var value of 0

So there’s a strong relationship between the binary response variable y_var, and the real-valued input, x_var.

We’ll be able to use Sklearn Logistic Regression to model that relationship.

Split data

Now, let’s split the data into training data and test data.

To split our data, we’ll use the train_test_split function from Scikit Learn.

from sklearn.model_selection import train_test_split
(X_train, X_test, y_train, y_test) = train_test_split(x_var.reshape(-1,1), y_var, test_size = .2)

Keep in mind that here, I’m reshaping x_var into a 2D shape before I split the data.

The reason for that, is because many Scikit learn tools (like Sklearn fit and Sklearn predict) require a 2-dimensional input for the “X” data.

After splitting, we have 4 datasets:

  • training features (X_train)
  • training target (y_train)
  • test features (X_test)
  • test target (y_test)

Initialize Model

Now, let’s initialize the LogisticRegression model:

from sklearn.linear_model import LogisticRegression
logistic_regressor = LogisticRegression()

Here, I’ve initialized a LogisticRegression object, and stored it with the name logistic_regressor. With this object, we’ll be able to train our model and make predictions.

Fit the Model

Now, we can train the model on the training data.

Here, we’ll use the “fit” method to train the model on the datasets X_train and y_train., y_train)


Now that the model is trained, we can make predictions with the model.

To do this, we’ll use the Sklearn predict method, and ask it to make new y predictions on the basis of new input values in X_test.



array([0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0,
       0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0])

Here, the 0/1 output values are predictions of y, based on any particular input value from X_test.

Leave your other questions in the comments below

Do you have other questions about the Sklearn Logistic Regression technique?

Is there something that I’ve missed?

If so, leave your questions in the comments section near the bottom of the page.

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In this tutorial, I’ve shown you how to use the Sklearn Logistic Regression method.

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Joshua Ebner

Joshua Ebner is the founder, CEO, and Chief Data Scientist of Sharp Sight. Prior to founding the company, Josh worked as a Data Scientist at Apple. He has a degree in Physics from Cornell University.

2 thoughts on “How to Use the Sklearn Logistic Regression Function”

  1. Hi Joshua,

    Always love your content from day one when i had started taking your course on Python, Numpy, Pandas, Matplotlib and Seaborn.

    is there any plan from your side to introduce the detail course on Machine Learning?


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