Features
In machine learning, features are the input variables used to train models. They represent the characteristics or attributes of the data that the algorithm learns from to make predictions. Features can be numerical, categorical, textual, or image-based and are crucial for the performance of a model.
Types of Features
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Numerical Features
- Continuous (e.g., height, weight, temperature)
- Discrete (e.g., number of children, age in years)
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Categorical Features
- Nominal (e.g., colors: red, blue, green)
- Ordinal (e.g., education levels: High School < Bachelor’s < Master’s)
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Boolean Features
- Binary values (e.g., 0/1, True/False)
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Time-Series Features
- Timestamped data (e.g., stock prices over time)
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Text Features
- Word embeddings, TF-IDF, bag-of-words, etc.
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Image Features
- Pixel intensities, edge detection, CNN-extracted features
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Audio Features
- Mel-frequency cepstral coefficients (MFCC), spectral features
Feature Engineering
To improve model performance, we can:
- Feature Scaling (Standardization, Normalization)
- Feature Selection (Removing irrelevant features)
- Feature Extraction (PCA, LDA, Autoencoders)
- Feature Encoding (One-hot encoding, Label encoding)
- Feature Transformation (Log transformation, Polynomial features)
What are Features in Machine Learning and Why it is Important?
In machine learning, features are individual independent variables that act like a input in your system. Actually, while making the predictions, models use such features to make the predictions. And using the feature engineering process, new features can also be obtained from old features in machine learning.
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📄️ Binarizer
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