Feature Engineering

Feature engineering is the process of selecting and creating the input descriptors for machine learning. Categorical data is converted to numeric values such as True=1 and False=0. Encoding creates indicators from images, words, numbers, or discrete categories.

⚙️ Ordinal Encoding: assign number to each option (e.g. 0=red, 1=blue)
⚙️ One-Hot Encoding: new binary (0 or 1) feature for each option
⚙️ Feature Hashing: compromise between ordinal and one-hot encoding

Feature generation may also create new data columns that are derived from the existing features. This may include a nonlinear transform of an individual feature or a product of two features.

Selection and creation of features is an important step in machine learning. Too many features may cause the classifier or regressor to increase the chances of predicting poorly. With many features, one of the inputs may be a bad value and cause a bad prediction. More features also take longer for data curation, training, and prediction. There are methods to rank the most important features for classification or regression.

There are methods to rank features in order of significance such as SHAP (SHapley Additive exPlanations). SHAP gives each feature a ranking to explain the output of a machine learning model or SelectKBest to rank the input features. Unimportant features are identified and removed to improve training time, reduce storage cost, and minimize deployment resources.

0:00 Feature Examples
3:10 Feature Engineering
4:17 Nonlinear Transform
6:51 Rolling Window
10:08 Categorical Data
12:10 Ordinal Encoding
14:44 One-Hot Encoding
16:58 Feature Hashing
20:18 Select K Best Features
25:32 SHAP: Shapley Additive Explanations
27:50 Feature Tools
29:10 Case Study 1
29:49 Case Study 2
30:35 Review

Machine Learning for Engineers: https://apmonitor.com/pds
Feature Engineering: https://apmonitor.com/pds/index.php/Main/FeatureEngineering

Видео Feature Engineering канала APMonitor.com