State True or False: Standardization of features is not required before training a Logistic regression model True False

State True or False: Standardization of features is not required before training a Logistic regression model Linear Regression errors values have to be normally distributed but in the case of Logistic Regression it is not the case Logistic Regression errors values have to be normally distributed but in the case of Linear Regression it is not the case Both Linear Regression and Logistic Regression error values have to be normally distributed Both Linear Regression and Logistic Regression error values have not to be normally distributed

(True/False) One of the requirements of logistic regression is that you need a variable with two classes.1 pointTrueFalse

You are tasked with enhancing the robustness of a logistic regression model by incorporating feature scaling. You're currently working with a dataset that has significantly varying scales among its features, which can affect the model's performance. Below is a preliminary setup for the logistic regression model. Identify the correct sequence of steps to integrate feature scaling into the modelling process.from sklearn.linear_model import LogisticRegressionfrom sklearn.model_selection import train_test_splitfrom sklearn.datasets import load_irisfrom sklearn.preprocessing import StandardScaler# Load the Iris datasetiris = load_iris()X = iris.datay = iris.target# Split data into training and testing setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)# Initialise the Logistic Regression modellr = LogisticRegression()# [Your Code Here] - Apply feature scaling to the training data# [Your Code Here] - Fit the model on the scaled training data# [Your Code Here] - Apply the same scaling to the test datascaler = StandardScaler()X_train_scaled = scaler.fit_transform(X_train)X_test_scaled = scaler.transform(X_test)lr.fit(X_train_scaled, y_train)scaler = StandardScaler()X_train_scaled = scaler.fit_transform(X_train)lr.fit(X_train_scaled, y_train)scaler = StandardScaler()X_test_scaled = scaler.fit_transform(X_test)scaler = StandardScaler()X_scaled = scaler.fit_transform(X)lr.fit(X_scaled, y)scaler = StandardScaler()X_train_scaled = scaler.transform(X_train)lr.fit(X_train_scaled, y_train)X_test_scaled = scaler.fit_transform(X_test)

Suppose I applied a logistic regression model on data and got training accuracy X and testing accuracy Y. Now I want to add few new features in data. Select option(s) which are correct in such case.Note: Consider remaining parameters are same.1. Training accuracy always decreases.2. Training accuracy always increases or remain same.3. Testing accuracy always decreases4. Testing accuracy always increases or remain same

What situations do you need data standardization in?You always have to standardize your dataWhen you have to compare different types of variablesWhen you have to compare same type of variablesYou never need to standardize your data