In human experience, features can be thought of as the observable or measurable characteristics that we use to interpret and make decisions about the world around us. These features can come from different sensory modalities or cognitive processes.

Visual modality: Color, shape, size, motion, texture (raw); patterns, symmetry, depth cues (derived)

Auditory modality: pitch, volume, tempo, rhythm (raw); speech patterns, tone of voice (derived)

haptics ? emotions ? language ?

Features represent the input variables used by a machine learning model to make predictions or classifications. They are the building blocks of the dataset and provide the information necessary for the model to learn relationships and patterns. Features can be:

Numerical: Continuous or discrete values (e.g., height, number of words).

Categorical: Representing distinct groups (e.g., color, category labels).

Derived: Transformed or engineered values combining raw data (e.g., ratios, log values).

Raw Features: Waveform amplitudes, signal energy.

Engineered Features: Mel-frequency cepstral coefficients (MFCCs), spectrogram data, pitch.

Context: In speech recognition, MFCCs are features extracted to characterize the audio signal.

Raw Features: Pixel intensity values, RGB color values.

Engineered Features: Haar features, Gabor wavelets, Histogram of gradients (HOG), edge counts, convolutional feature maps.

Context: In object detection, pixel patterns or edge-based features help detect objects in the image.

Raw Features: occurence of specific character sequences, word or token counts, sequence length

Engineered Features: Word "embeddings" (e.g., Word2Vec, BERT embeddings),

Context: In sentiment analysis, embeddings provide dense, meaningful representations of text features.

Feature detection is the process of identifying significant patterns, structures, or attributes in raw data to aid analysis and decision-making. 

Feature selection involves choosing the most relevant features from a dataset to improve model accuracy, reduce overfitting, and enhance computational efficiency. Techniques include filters (e.g., chi-square tests), wrappers (e.g., recursive feature elimination), and embedded methods like LASSO. Boosting algorithms (e.g., AdaBoost, Gradient Boosting, XGBoost) also inherently perform feature selection by iteratively focusing on features with the highest predictive power.