Classification using Logistic Regression

The difference between Linear and Logistic Regression, Image from

Binary Classification

This was the hypothesis function for Linear Regression where theta was a vector of parameters
This is the hypothesis function where the bottom function is called the sigmoid function or the logistic function

Cost/Loss Function

This is the i-th training example, this can be only one value or this can be a vector of values if you have multiple features
This is the answer or the output of i-th training example
This is the cost function for Logistic Regression, Image from
This is the gradient descent algorithm where alpha is the learning rate and we update all the parameters theta simultaneously in every iteration of Gradient Descent

Python Implementation

import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
data_frame = pd.read_csv('/Social_Network_Ads.csv')
data_frame.Gender = pd.Categorical(data_frame.Gender).codes
X = data_frame[['Gender', 'Age', 'EstimatedSalary']]
y = data_frame['Purchased']
X = X.to_numpy()
y = y.to_numpy()
scaler = MinMaxScaler()
X = scaler.transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LogisticRegression(), y_train)
def pred(x_value):
x_value = scaler.transform([x_value])
print(model.score(X_train, y_train))

Multiclass Classification (One vs All Algorithm)


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Ahaan Pandya

Ahaan Pandya

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