Classification
Last updated
Last updated
Definition: Classification is a type of predictive modeling that aims to categorize or assign observations or instances to a predefined set of classes or categories. It's used to predict the category or class of a new dataset, based on training from historical data.
Example: Email spam detection, sentiment analysis, disease diagnosis (e.g., classifying a patient as having a particular disease or not based on symptoms).
Steps:
1. Select Independent Columns (X):
Identify and choose the independent columns in your dataset.
These columns, often referred to as features or predictors, are the variables that will be used to predict the dependent variable(Y).
2. Select Dependent Column (Y):
Identify the dependent variable or target variable (Y) that you aim to predict.
This column represents the output or the variable to be predicted based on the other independent variables (X).
3. Cross-Validation:
Determine the level or number of folds for cross-validation. Cross-validation is a resampling technique used to assess how the results of a predictive model will generalize to an independent dataset.
Common methods include k-fold cross-validation, where the dataset is divided into k subsets or folds. The model is trained on k-1 folds and tested on the remaining fold, repeated k times.
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