Logistic regression is a popular machine learning algorithm that is widely used for binary classification problems. It is a statistical method that models the relationship between a dependent binary variable and one or more independent variables. In this article, we will explore the concept of logistic regression and provide an example code in Python to demonstrate how it works.

What is Logistic Regression?

Logistic regression is a statistical technique that is used for binary classification problems, where the dependent variable can take only two possible values, usually represented as 0 and 1. It is a supervised learning algorithm that is used to predict the probability of an event occurring based on the input variables. The output of logistic regression is a probability value between 0 and 1, which can be converted to a binary value using a threshold value.

The logistic regression model assumes that there is a linear relationship between the input variables and the logarithm of the odds ratio of the dependent variable. The equation for logistic regression can be represented as:

p(y=1|x) = 1 / (1 + e^(-z))

where:

• p(y=1|x) is the probability of the dependent variable taking the value 1 given the input variables
• z is the linear combination of the input variables and their coefficients

Logistic Regression Example Code in Python

To demonstrate how logistic regression works, we will use the famous Iris dataset, which contains information about the sepal length, sepal width, petal length, and petal width of three different species of iris flowers. Our goal is to build a logistic regression model to predict whether a flower is of the Iris Virginica species or not.

First, we need to import the necessary libraries and load the dataset.

from sklearn.datasets import load_iris
import pandas as pd

iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target

Next, we will create a binary variable that indicates whether the flower is of the Iris Virginica species or not.

df['target'] = df['target'].apply(lambda x: 1 if x==2 else 0)

Then, we will split the dataset into training and testing sets.

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(df[iris.feature_names], df['target'], test_size=0.2, random_state=0)

Next, we will create a logistic regression model using scikit-learn’s LogisticRegression class.

from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
model.fit(X_train, y_train)

Finally, we will evaluate the model’s performance on the testing set by calculating the accuracy score and the confusion matrix.

from sklearn.metrics import accuracy_score, confusion_matrix

y_pred = model.predict(X_test)
acc = accuracy_score(y_test, y_pred)
cm = confusion_matrix(y_test, y_pred)
print("Accuracy:", acc)
print("Confusion Matrix:", cm)

Conclusion

Logistic regression is a powerful machine learning algorithm that is widely used for binary classification problems. In this article, we provided an overview of logistic regression, discussed its equation, and provided an example code in Python to demonstrate how it works. We hope this article has been helpful in understanding the basics of logistic regression and how it can be used for binary classification problems.