#### Tag: Machine Learning

This is a generic, practical approach that can be applied to most machine learning problems: 1-Categorize the problem The next step is to categorize the problem. Categorize by…

In the 1970s, two psychologists proved, once and for all, that humans are not rational creatures. Daniel Kahneman and Amos Tversky discovered “cognitive biases,” showing that that humans…

I believe, therefore it’s true In statistical analysis selection bias means that the sample you have chosen is not representative of the population you want to look at. Let…

You may have heard of the famous book The Signal and the Noise by Nate Silver. In predictive modeling, you can think of the signal as the true…

In pattern recognition information retrieval and binary classification, precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known…

The Bias-Variance dilemma is relevant for supervised machine learning. It’s a way to diagnose an algorithm performance by breaking down its prediction error. There are three types of…

Standardization Standardization (or Z-score normalization) is the process of rescaling the features so that they’ll have the properties of a Gaussian distribution with μ=0 and σ=1 where μ…

In machine learning dimensionality simply refers to the number of features(I.e. input variables in the datasets). when the number of features is very large relative to the number…

There are two types of models, parametric and non-parametric, let’s start with parametric models. Parametric model A learning model that summarizes data with a set of parameters of…