Parametric machine learning algorithm– Linear Regression, Logistic Regression Non-Parametric machine learning algorithm – Decision Trees, SVM, Neural Network

The algorithm which does not make strong assumptions are a non-parametric algorithm and they are free to learn from training data. The algorithm that makes strong assumptions are parametric and it involves

select the form for the function and learn the coefficients

select the form for the function and learn the coefficients

It works better when we remove the attributes which are unrelated to the output variable and highly co-related variable to each other.

False Positive – A cancer screening test comes back positive, but you don’t have cancer

False Negative – A cancer screening test comes back negative, but you have cancer

True Positive – A Cancer Screening test comes back positive, and you have cancer

True Negative – A Cancer Screening test comes back negative, and you don’t have cancer

False Negative – A cancer screening test comes back negative, but you have cancer

True Positive – A Cancer Screening test comes back positive, and you have cancer

True Negative – A Cancer Screening test comes back negative, and you don’t have cancer

Sensitivity means “proportion of actual positives that are correctly classified” in other words “True Positive”

Specificity means “proportion of actual negatives that are correctly classified” “True Negative”

Specificity means “proportion of actual negatives that are correctly classified” “True Negative”