To measure how well our models are performing we may need to define some metrics. One of the metrics which is quite straight forward is accuracy. Accuracy is defined as the number of correctly classified points divided by the total Read more
Median Absolute deviation is one of the other techniques specifically used for analyzing the performance of regression models. Computing MAD of errors For a univariate data set X1, X2, …, Xn, the MAD is defined as the median of the Read more
Writing clean code is more of an art rather than a science. So What really makes code cleaner?. In this series called Clean Code Concepts, we investigate some of the ways to write code in a clean way. There are several Read more
R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression. This metric is specifically designed for Read more
We can plot error distributions like probability density functionand cumulative density function and make important deductionsbased on it. We can use plot Probability Density functions(PDF) and Cumulative density function (CDF) by using the error function as a random variable Using Read more
Logarithmic loss (or cross-entropy) measures the performance of a classification model where the prediction input is a probability value between 0 and 1. The goal of our machine learning models is to minimize this value. It is also heavily used Read more
For binary classification problems, a good way to measure the performance of a model is by finding out AUC (Area Under The Curve) of ROC (Receiver Operating Characteristics). What is a ROC curve? It is a plot of True Positive Read more
Confusion Matrix Confusion Matrix is a group or matrix of metrics in supervised learning scenarios which determine how good a model is in predictions. Lets consider a binary classifier with results 1 or 0. A confusion matrix here will be Read more
Hypothesis Testing Hypothesis tests, also called significance tests, are ubiquitous in the traditional statistical analysis of published research. Their purpose is to help you learn whether random chance might be responsible for an observed effect. KEY TERMS Null hypothesis The Read more
Welcome to part 1 of writing python like a pro. This series is definitely part of the advanced python series. It assumes you already know python and are definitely comfortable with it. However, you are a person who wants to Read more