Sklearn diabetes dataset

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset .

Machine learning methods are widely used in predicting diabetes, and they get preferable results. Decision tree is one of popular machine learning methods in medical field , which has grateful classification power. Random forest generates many decision trees.

Recently, numerous algorithms are used to predict diabetes, including the traditional machine learning method (Kavakiotis et al., 2017), such as support vector machine (SVM), decision tree (DT), logistic regression and so on.6 Kas 2018

Recently, numerous algorithms are used to predict diabetes, including the traditional machine learning method (Kavakiotis et al., 2017), such as support vector machine (SVM), decision tree (DT), logistic regression and so on.6 Kas 2018

Logistic Regression is one of the most common classification algorithms. The default value of C=1 provides with 78% accuracy on the training and 77% accuracy on the test set. Using C=0.01 results in lower accuracy on both the training and the test sets.

Logistic Regression is one of the most common classification algorithms. The default value of C=1 provides with 78% accuracy on the training and 77% accuracy on the test set. Using C=0.01 results in lower accuracy on both the training and the test sets.