Python – Training a Model using Logistic Regression

In this post, you will learn about how to train a model using machine learning algorithm such as Logistic Regression.

Here is the code we can use for fitting a model using Logistic Regression. We will use IRIS data set for training the model.

Loading SkLearn Modules / Classes

First and foremost, we will load the appropriate packages, sklearn modules and classes.

# Importing basic packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Importing Sklearn module and classes
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
from sklearn import datasets
from sklearn.model_selection import train_test_split

Data Loading

As a next step, we will load the dataset and do the data preparation.

iris = datasets.load_iris()
X =[:, [0, 2]]
Y =

Create Training / Test Data

Next step is to create a train and test split. Note the stratification parameter. This is used to ensure that class distribution in training / test split remains consistent / balanced.

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=1, stratify=Y)

Perform Feature Scaling

Next step is to perform feature scaling in order to make sure features are in fixed range irrespective of their values / units etc.

sc = StandardScaler()
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

Train a Logistic Regression Model

Next step is to train a logistic regression model. The following needs to be noted while using LogisticRegression algorithm sklearn.linear_model implementation:

  • Usage of C parameters. Smaller values of C specify stronger regularization.
  • The multi_class parameter is assigned to ‘ovr‘. It represents one-vs-rest algorithm to be used. Other option is multinomial.
  • The solver parameter is assigned to ‘lbfsg‘. Other solvers which can be used are newton-cg, sag, saga, lib linear
# Create an instance of LogisticRegression classifier
lr = LogisticRegression(C=100.0, random_state=1, solver='lbfgs', multi_class='ovr')

# Fit the model
#, Y_train)
Fig 1. Logistic Regression Model being Fit

Measure Model Performance

Next step is to measure the model performance of the model trained using LogisticRegression as shown above.

# Create the predictions
Y_predict = lr.predict(X_test_std)

# Use metrics.accuracy_score to measure the score
print("LogisticRegression Accuracy %.3f" %metrics.accuracy_score(Y_test, Y_predict))
Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data Science and Machine Learning / Deep Learning. In addition, I am also passionate about various different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia etc and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc. I would love to connect with you on Linkedin.
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