jarvis.ai.pkgs.sklearn.classification

Simple ML models for classifcation and regression.

Designed for educational purposes only

Module Contents

Functions

classify_roc_ml(X=[], y=[], classes=[0, 1, 2], names=['High val', 'Low val', ''], n_plot=1, method='', preprocess=True, plot=False, test_size=0.1)

Classifcation module for ROC curve for upto three classes.

classification(X=[], Y=[], tol=100, plot=False, preprocess=True, models=simple_class_models, model_name='my_model', save_model=False)

Quickly train some of the classifcation algorithms in scikit-learn.

Attributes

simple_class_models

jarvis.ai.pkgs.sklearn.classification.simple_class_models
jarvis.ai.pkgs.sklearn.classification.classify_roc_ml(X=[], y=[], classes=[0, 1, 2], names=['High val', 'Low val', ''], n_plot=1, method='', preprocess=True, plot=False, test_size=0.1)[source]

Classifcation module for ROC curve for upto three classes.

It can be expanded in more classes as well. Args:

X: input feature vectors

y: target data obtained from binary_class_dat

classes: dummy classes

names: name holders for the target data

method: ML method

preprocess: whether to apply standard preprocessing techniques

plot: whether to plot the ROC curve

jarvis.ai.pkgs.sklearn.classification.classification(X=[], Y=[], tol=100, plot=False, preprocess=True, models=simple_class_models, model_name='my_model', save_model=False)[source]

Quickly train some of the classifcation algorithms in scikit-learn.