PYTHON Tutorial

Feature Engineering

Feature engineering is the process of transforming raw data into features that are more suitable for machine learning models. It involves three key steps: feature extraction, feature selection, and dimensionality reduction.

Feature Extraction

Feature extraction is the process of creating new features from the raw data. This can be done through a variety of techniques, such as:

  • One-hot encoding: Converting categorical variables into binary vectors.
  • Binning: Grouping continuous variables into discrete bins.
  • Normalization: Scaling continuous variables to have a mean of 0 and a standard deviation of 1.

Feature Selection

Feature selection is the process of selecting the most relevant features for the machine learning model. This can be done through a variety of techniques, such as:

  • Filter methods: Ranking features based on their individual properties, such as variance or correlation.
  • Wrapper methods: Evaluating the performance of the model on different subsets of features.
  • Embedded methods: Selecting features as part of the model training process.

Dimensionality Reduction

Dimensionality reduction is the process of reducing the number of features in the dataset. This can be done through a variety of techniques, such as:

  • Principal component analysis (PCA): Transforming the features into a new set of uncorrelated features that capture the maximum variance in the data.
  • Singular value decomposition (SVD): Similar to PCA, but can be used on non-square matrices.
  • t-SNE: A non-linear dimensionality reduction technique that is useful for visualizing high-dimensional data.

Python Example

The following Python example demonstrates how to create new features from raw data to improve model performance:

import pandas as pd
from sklearn.preprocessing import OneHotEncoder, StandardScaler

# Load the raw data
df = pd.read_csv('raw_data.csv')

# Create new features using one-hot encoding
encoder = OneHotEncoder()
df = pd.concat([df, pd.DataFrame(encoder.fit_transform(df['category']), columns=encoder.categories_[0])], axis=1)

# Normalize the continuous features
scaler = StandardScaler()
df['numeric_feature'] = scaler.fit_transform(df['numeric_feature'])

# Use the new features to train a machine learning model
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(df.drop('target', axis=1), df['target'])

By creating new features and selecting the most relevant ones, we can improve the performance of our machine learning model.