Feature Engineering Course

Learning Objectives

Define “feature” in the context of machine learning, including when to apply different transformations.
Implement and compare common feature extraction methods (numeric, categorical, textual) in Python.
Recognize the relationship between feature engineering, model performance, and best practices.
Evaluate and compare extracted features using statistical measures while avoiding pitfalls like data leakage.

Key Concepts & Terminology

Loading concepts...

Please wait while content loads...

Interactive Session

Access Interactive Code Demo

Basic Python Feature Extraction Examples

Loading code...

Please wait while content loads...

Practice Exercise

Load a dataset with numeric features in Python (e.g., pandas.read_csv('data.csv')).
Identify numeric features that might need transformations (e.g., log or power transform).
Apply one-hot encoding to a categorical column and analyze the resulting new columns.
Create a simple text-based feature (e.g., TF-IDF or bag-of-words) on a small text column and print a portion of the resulting feature matrix.

Quick Quiz

1. Which of the following is NOT a common approach for handling categorical features?

One-hot encoding Target encoding Chi-square encoding Label encoding

2. Which of these transformations would be most appropriate for a numeric feature with a long-tail distribution?

Min-max scaling Logarithmic transformation Z-score normalization Polynomial expansion

3. What problem can arise when applying one-hot encoding to a categorical feature with many unique values?

Data leakage Feature redundancy The curse of dimensionality Normalization error

4. When using word embeddings like Word2Vec for text feature extraction, what is a key advantage compared to TF-IDF?

They require less computing power They capture semantic relationships between words They always result in better model performance They produce fewer features

5. Which of these techniques would be most appropriate for extracting features from time-series data?

One-hot encoding Rolling statistics (mean, variance over windows) SIFT descriptors Term frequency analysis

6. In the context of feature scaling, what is the primary purpose of StandardScaler?

To transform features to have zero mean and unit variance To bound all values between 0 and 1 To handle missing values in the dataset To remove outliers from the dataset

7. What does “data leakage” refer to in feature engineering?

When sensitive information is accidentally included in features When features incorporate information that wouldn't be available at prediction time When features have too many missing values When features are highly correlated with each other

Course Modules

Module 1: Intro to Feature Extraction