Machine & Deep Learning Compendium
The Machine & Deep Learning Compendium
Types Of Machine Learning
Data Science Tools
Data Science Management
Probability & Statistics
Multi Label Classification
Normalization & Scaling
Hyper Parameter Optimization
Multi CPU Processing
Classic Machine Learning
Active Learning Algorithms
Linear Separator Algorithms
Dimensionality Reduction Methods
Genetic Algorithms & Genetic Programming
Learning Classifier Systems
Digital Signal Processing (DSP)
Propensity Score Matching
Natural Language Processing
Business Domains For Data Science
- no permission doc
Categorical data are variables that contain label values rather than numeric values.
The number of possible values is often limited to a fixed set.
Categorical variables are often called
labels, usually discrete values such as gender, country of origin, marital status, high-school graduate
Continuous (the opposite of discrete): real-number values, measured on a continuous scale: height, weight.
In order to compute a regression, categorical predictors must be re-expressed as numeric: some form of indicator variables (0/1) with a separate indicator for each level of the factor.
Discrete with many values are often treated as continuous, i.e. zone numbers - > binary
Nominal(weather), ordinal(order var 1,2,3), interval(range),