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)

The correct sequence of steps to integrate feature scaling into the modelling process is as follows:

1. Initialize the StandardScaler: This is done by creating an instance of the StandardScaler class from the sklearn.preprocessing module.

scaler = StandardScaler()

2. Fit the scaler to the training data and transform it: The fit method computes the mean and standard deviation, which will be used later for scaling. The transform method scales the features using the computed mean and standard deviation.

X_train_scaled = scaler.fit_transform(X_train)

3. Fit the logistic regression model on the scaled training data: This is done using the fit method of the LogisticRegression instance.

lr.fit(X_train_scaled, y_train)

4. Apply the same scaling to the test data: It's important to use the same scaling on the test data for consistent results. This is done using the transform method of the scaler instance. Note that we only use transform here, not fit_transform, because we want to use the same scaling parameters as the ones used for the training data.

X_test_scaled = scaler.transform(X_test)

So, the correct code sequence is:

# Initialise the StandardScaler
scaler = StandardScaler()

# Fit the scaler to the training data and transform it
X_train_scaled = scaler.fit_transform(X_train)

# Fit the logistic regression model on the scaled training data
lr.fit(X_train_scaled, y_train)

# Apply the same scaling to the test data
X_test_scaled = scaler.transform(X_test)

This sequence ensures that the same scaling is applied to both the training and test data, which is crucial for the performance of the logistic regression model.