breast_cancer Using LogisticRegression from sklearn.linear_model for breast cancer libraries: import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn import metrics from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
Explaining the algorithm :
Logistic regression is a statistical model that is used for binary classification. It is a special case of linear regression, where the dependent variable is categorical in nature. Logistic regression predicts the probability of an event occurrence, given a set of independent variables.
The logistic regression algorithm from sklearn is implemented in the LogisticRegression class. This class can be used to fit a logistic regression model to a dataset, and to make predictions on new data. The LogisticRegression class has a number of hyperparameters that can be tuned to improve the performance of the model. These hyperparameters include the regularization strength, the solver, and the number of features to use.
The LogisticRegression class also has a number of methods that can be used to evaluate the performance of the model. These methods include the score() method, which returns the accuracy of the model on a given dataset, and the predict_proba() method, which returns the probability of each class for each data point.
Logistic regression is a powerful tool for binary classification. It is a relatively simple model to understand and implement, and it can be very effective for a wide variety of tasks. However, it is important to note that logistic regression is not a perfect model. It can be sensitive to outliers and noise in the data, and it can be difficult to interpret the results of the model.