Ridge Regression is a Linear Regression model use to solve some of the problems of Ordinary Least Squares by imposing penalty on regression coefficients. What is Ridge Regression? We have seen Ordinary Least Squares. Suppose we have independent variable X and dependent variable Y. we can write and our Objective Function is Suppose we have…
Clustering: A Hospital Care chain wants to open a series of Emergency-Care wards within a region. We assume that the hospital knows the location of all the maximum accident-prone areas in the region. They have to decide the number of Emergency Units to be opened and the location of these Emergency Units, so that all the…
Bayes theorem (alternatively Bayes’ law or Bayes’ rule) describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if cancer is related to age, then, using Bayes’ theorem, a person’s age can be used to more accurately assess the probability that they have cancer,…
We often judge people by their vicinity to the group of people they live with. People who belong to a particular group are usually considered similar based on the characteristics they possess. This is the simple principle on which the KNN algorithm works – “Birds of the same feather flock together.” The abbreviation KNN stands…
Logistic regression is one of the most popular Machine Learning algorithms, which comes under the Supervised Learning technique. It is a classification algorithm. It is used to predict a binary outcome based on a set of independent variables that translates the input to 0 or 1. Ok, so what does this mean? A binary outcome is…
Linear Regression is the supervised Machine Learning model in which the model finds the best fit linear line between the independent and dependent variable i.e. it finds the linear relationship between the dependent and independent variable. It is used for generating continuous values like the price of the house, income, population, etc The linear regression analysis is used…
Kernel functions or Kernel trick can also be regarded as the tuning parameters in an SVM model. They are responsible for removing the computational requirement to achieve the higher dimensional vector space and deal with the non-linear separable data. Here’s our post on the SVM model. The SVM kernel could be a function that takes low…
Support Vector Machines are a set of supervised learning methods used for classification, regression, and outlier detection. A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyper-plane. The idea of SVM is simple: It takes the past data as an input and outputs a line or a hyper-plane which separates…
Different stages in machine learning model: Please refer to our section on machine learning models. 1. Gathering Data First thing first. You need to understand the Business Problem you are facing. You have to consider what the main goals of your problem are. Data is power. When the problem is clear, and an appropriate machine…
There are four main test statistics you can use in a hypothesis test. Which one you use depends on which statistical test you run. Please refer to our earlier post to understand the terminologies of Hypothesis Testing. Hypothesis Test Test Statistic Z-Test Z-Score T-Test T-Score ANOVA F-statistic Chi-Square Test Chi-square statistic Different test statistics The calculated…
