Feature Construction :

Feature construction is the application of a set of constructive operators to a set of existing features resulting in the construction of new features.

Feature construction is the process of creating new features for a machine-learning model from existing data. These new features, also known as derived features or artificial features, can be created by combining, manipulating, or aggregating existing features in various ways. The goal of feature construction is to improve the performance of the machine learning model by providing it with more relevant and informative features.

Many different techniques can be used for feature construction, including:

  1. Feature scaling: This involves transforming the values of a feature so that they are on the same scale. This can be useful if the scale of a feature is significantly different from the scale of other features, as it can help the model learn more effectively.

2. Feature selection: This involves selecting a subset of the most relevant or informative features from the dataset for use in the model. This can help reduce overfitting and improve the model’s generalization to new data.

3. Feature engineering: This involves creating new features from existing data by combining, manipulating, or aggregating existing features in various ways. This can help the model learn more effectively by providing it with more relevant and informative features.

It’s important to note that feature construction is an iterative process, and it may require experimentation and trial and error to find the most effective features for a particular machine-learning task.

  • In simple terms, the addition of many columns into one new feature.

Feature Splitting:

Splitting features. There are two ways to split a line feature. You can manually split a line by clicking it at the point where you want to split it into two lines, or you can specify where to split a line based on a distance or percentage.

Feature splitting is the process of dividing a feature into smaller sub-features or groups of data. This can be useful for a variety of reasons, including improving the performance of a machine learning model, reducing the complexity of the model, and making the model more interpretable.

There are several different techniques that can be used for feature splitting, including:

  1. Binning: This involves dividing a continuous feature into a set of bins or intervals. This can be useful for converting a continuous feature into a categorical feature, which may be easier for a model to learn.

2. One-hot encoding: This involves converting a categorical feature with multiple categories into multiple binary features, each representing a single category. This can be useful for converting a categorical feature into a form that is more suitable for use in a machine-learning model.

3. Interaction terms: This involves creating new features by combining two or more existing features in various ways, such as multiplying or adding them together. This can be useful for capturing non-linear relationships between features and improving the performance of the model.

It’s important to note that feature splitting is an iterative process, and it may require experimentation and trial and error to find the most effective way to split a particular feature.

-In simple terms, dividing the columns into many columns.

The provided code is part of a data preprocessing pipeline for a machine learning project, specifically in the context of predicting survival on the Titanic. Let’s break down the code and explain each part:

Datasets : https://www.kaggle.com/datasets/hesh97/titanicdataset-traincsv
import numpy as np
import pandas as pd

from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression

import seaborn as sns

# Load the Titanic dataset and select relevant columns
df = pd.read_csv('train.csv')[['Age','Pclass','SibSp','Parch','Survived']]

# Display the first 5 rows of the DataFrame
df.head()

Explanation:

  • The code starts by importing necessary libraries (numpy, pandas, seaborn, sklearn).
  • It reads the Titanic dataset from a CSV file and selects specific columns (‘Age’, ‘Pclass’, ‘SibSp’, ‘Parch’, ‘Survived’).
  • The head() function is used to display the first five rows of the DataFrame.
# Drop rows with missing values (NaN) in any column
df.dropna(inplace=True)

# Display the first 5 rows after dropping missing values
df.head()

Explanation:

  • Rows with missing values are removed using the dropna() function with inplace=True.
  • The head() function is again used to display the first five rows of the cleaned DataFrame.
# Separate features (X) and target variable (y)
X = df.iloc[:, 0:4]
y = df.iloc[:, -1]

# Display the first 5 rows of features (X)
X.head()

Explanation:

  • The dataset is split into features (X) and the target variable (y).
  • X includes columns ‘Age’, ‘Pclass’, ‘SibSp’, and ‘Parch’.
  • The head() function displays the first five rows of the features.
# Cross-validate a logistic regression model and calculate the mean accuracy
np.mean(cross_val_score(LogisticRegression(), X, y, scoring='accuracy', cv=20))

Explanation:

  • Cross-validation is performed using logistic regression with 20 folds, and the mean accuracy is calculated.
# Feature Construction: Create a new feature 'Family_size' by summing 'SibSp', 'Parch', and 1
X['Family_size'] = X['SibSp'] + X['Parch'] + 1

# Display the first 5 rows with the new feature
X.head()

Explanation:

  • A new feature, ‘Family_size’, is created by summing ‘SibSp’, ‘Parch’, and 1.
  • The head() function displays the first five rows with the new feature.
# Apply a custom function 'myfunc' to create a categorical feature 'Family_type'
def myfunc(num):
    if num == 1:
        return 0  # alone
    elif 1 < num <= 4:
        return 1  # small family
    else:
        return 2  # large family

# Apply 'myfunc' to 'Family_size' and create 'Family_type'
X['Family_type'] = X['Family_size'].apply(myfunc)

# Display the first 5 rows with the new categorical feature
X.head()

Explanation:

  • A custom function myfunc is defined to categorize family sizes into three types: alone, small family, and large family.
  • The function is applied to the ‘Family_size’ column to create a new categorical feature ‘Family_type’.
  • The head() function displays the first five rows with the new categorical feature.
# Feature Splitting: Drop unnecessary columns 'SibSp', 'Parch', and 'Family_size'
X.drop(columns=['SibSp', 'Parch', 'Family_size'], inplace=True)

# Display the first 5 rows after dropping columns
X.head()

Explanation:

  • Unnecessary columns ‘SibSp’, ‘Parch’, and ‘Family_size’ are dropped from the features (X).
  • The head() function displays the first five rows after dropping columns.
# Cross-validate logistic regression again with the updated features and calculate the mean accuracy
np.mean(cross_val_score(LogisticRegression(), X, y, scoring='accuracy', cv=20))

Explanation:

  • Cross-validation is performed again with logistic regression using the updated features, and the mean accuracy is calculated.
# Feature Engineering: Extract 'Title' from 'Name' and create a new feature 'Is_Married'
df = pd.read_csv('train.csv')
df['Title'] = df['Name'].str.split(', ', expand=True)[1].str.split('.', expand=True)[0]

# Display the mean survival rate for each title
(df.groupby('Title').mean()['Survived']).sort_values(ascending=False)

Explanation:

  • A new DataFrame is loaded, and a ‘Title’ column is extracted from the ‘Name’ column using string manipulation.
  • The mean survival rate is calculated for each title and displayed in descending order.
# Create a new binary feature 'Is_Married' based on the 'Title'
df['Is_Married'] = 0
df['Is_Married'].loc[df['Title'] == 'Mrs'] = 1

# Display the 'Is_Married' column
df['Is_Married']

Explanation:

  • A new binary feature ‘Is_Married’ is created based on the ‘Title’. If the title is ‘Mrs’, ‘Is_Married’ is set to 1.
  • The loc function is used to locate and modify specific values in the ‘Is_Married’ column.

This code demonstrates various data preprocessing steps, including handling missing values, creating new features, and transforming categorical features, in preparation for machine learning model training.

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