geochemistrypi.data_mining.process package# Submodules# geochemistrypi.data_mining.process.classify module# class ClassificationModelSelection(model_name: str)[source]# Bases: ModelSelectionBase Simulate the normal way of training classification algorithms. activate# Dispatch methods based on type signature See also Dispatcher geochemistrypi.data_mining.process.cluster module# class ClusteringModelSelection(model_name: str)[source]# Bases: ModelSelectionBase Simulate the normal way of invoking scikit-learn clustering algorithms. activate(X: DataFrame, y: DataFrame | None = None, X_train: DataFrame | None = None, X_test: DataFrame | None = None, y_train: DataFrame | None = None, y_test: DataFrame | None = None) → None[source]# Train by Scikit-learn framework. geochemistrypi.data_mining.process.decompose module# class DecompositionModelSelection(model_name: str)[source]# Bases: ModelSelectionBase Simulate the normal way of invoking scikit-learn decomposition algorithms. activate(X: DataFrame, y: DataFrame | None = None, X_train: DataFrame | None = None, X_test: DataFrame | None = None, y_train: DataFrame | None = None, y_test: DataFrame | None = None) → None[source]# Train by Scikit-learn framework. geochemistrypi.data_mining.process.regress module# class RegressionModelSelection(model_name: str)[source]# Bases: ModelSelectionBase Simulate the normal way of training regression algorithms. activate# Dispatch methods based on type signature See also Dispatcher Module contents#