hastie
There are 0 repository under hastie topic.
- Language:Jupyter Notebook 9
- Language:Jupyter Notebook 6
ablanco1950 / HASTIE_Corrected_DecisionTree
Using the decision tree technique based on entropy calculation, this application calculates the hit rate of the HASTIE file with a hit rate higher than 99%
Language:Python 0Taking into account that the accuracy of statistical results depend on the accuracy of the input data, not only on the algorithm, a Hastie file has been created in which all the records have the correct class assigned and tests of hit rates and sensitivity have been carried out
Language:Python 0HASTIE_NAIVEBAYES: from the Hastie_10_2.csv file obtained by the procedure described in https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_hastie_10_2.html, obtains a success rate in the training of 88% and 84% in the test. The main difference is that in the statistical process, each field is sampled differently according to its contribution to the hit rate.
Language:Java 0- Statistical_Learning_Python
AhmedaCheick / Statistical_Learning_Python
Introduction to Statistical Learning by Hastie, Tibshirani James, and Witten chapters' summary and lab solutions using Python3.
Language:Jupyter Notebook 0 Using the Sklearn classifiers: Naive Bayes, Random Forest, Adaboost, Gradient Boost, Logistic Regression and Decision Tree good success rates are observed in a very simple manner. In this work sensitivity is also considered. Treating each record individually, differences are found in the results for each record depending on the model used, which is hidden in treatments that compute a total volume of data
Language:Python
