Introduction to Building Machine Learning Applications Building machine learning applications requires a thorough understanding of the fundamentals of machine learning and software development. This section will provide an overview of the key considerations and best practices for building machine learning applications. Best Practices for Data Preprocessing Data preprocessing is a 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 absolute deviations from the data’s median, X_median = median(X) Median 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 regression-based algorithms where the output is a real value. Computing 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 PDF of error distribution An ideal pdf for error distributions 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 in Kaggle competitions to estimate the score of submissions. A 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 Rate(TPR) vs FPR at various thresholding levels. Let’s understand this Read more