Iris Species Predictor Iris species predictor app is used to classify iris species using their sepal length, sepal width, petal length and petal width created using python’s scikit-learn, fastapi, numpy and joblib packages. Dataset Description:- This famous (Fisher’s or Anderson’s) iris data set gives the measurements in centimeters of the variables sepal length and width and petal    

Package for interpreting scikit-learn’s decision tree and random forest predictions. Allows decomposing each prediction into bias and feature contribution components as described in http://blog.datadive.net/interpreting-random-forests/. For a dataset with n features, each prediction on the dataset is decomposed as prediction = bias + feature_1_contribution + … + feature_n_contribution. It works on scikit-learn’s DecisionTreeRegressor DecisionTreeClassifier ExtraTreeRegressor ExtraTreeClassifier RandomForestRegressor RandomForestClassifier ExtraTreesRegressor ExtraTreesClassifier Free software: BSD license Dependencies Installation The easiest way to install the package is via pip: $ pip install treeinterpreter Usage […]