inputvectoren

Inputvectoren, or input vectors, are the numerical representations of the features describing a single observation used as input to a model, algorithm, or system. Typically, an inputvector is denoted x and belongs to a real-valued vector space R^n, where n is the number of features. The elements x_i correspond to different attributes or measurements, such as age, temperature, pixel intensity, or encoded categories. Raw data are often transformed into inputvectoren through preprocessing and feature engineering, including normalization, imputation of missing values, and encoding of categorical variables (for example one-hot or embedding representations).

Inputvectors can be dense (most elements nonzero) or sparse (most elements zero). In natural language processing, high-dimensional sparse vectors are common (e.g., one-hot encodings); in modern pipelines, dense embeddings reduce dimensionality.

Usage spans several domains. In supervised learning, inputvectoren serve as the independent variables that drive predictions; in control theory, they may represent control inputs that influence system dynamics; in signal processing, they can be samples of a signal over a time window.

Practical considerations include the choice of features, scaling, and handling missing data, all of which affect model performance. High dimensionality can lead to inefficiency and overfitting, so dimensionality reduction or feature selection is often employed. Models assume the input vector is properly aligned across samples, with a consistent ordering of features.

Example: for a dataset with features [age, height, gender encoded as 0/1, annual income], an inputvector could be [35, 175, 0, 55000].