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Parametric Machine Learning

I have a finite number of parameters for example in case of linear regression, we need to find slope(m) and intercept(c), where ever we need to find the parameters we can say it parametric Machine learning algorithm. We can also say, when we work with assumptions like data should be normally distributed, multi collinearity etc…Continue reading “Parametric Machine Learning”

Hyper parameter tuning

Before going to this blog make sure you are aware about any one of the algorithms like Decision tree, Random forest.etc…. Below are the few parameters that we need for most of the algorithms. Maximum number of leaves per tree Depth of trees. Number of trees in Random forest Learning rate L1 and L2 regularizationContinue reading “Hyper parameter tuning”

Ensemble

I want to buy a home, so I approached one consultancy and they asked couple of questions and suggested the place and the builder. Do you think taking suggestions from one consultancy is a good idea? Absolutely NO.. rather I will take opinions from different consultancy and will chose the best fit for me basedContinue reading “Ensemble”

Binomial distribution

A binomial distribution is a probability of SUCCESS or FAILURE in an experiment that is repeated many times. It is call Binominal because we will have only two outcomes. Binomial is a discrete because the values are fixed For Eg: I want to roll a dice and expecting six, I will either get six (SUCCESS)Continue reading “Binomial distribution”

How to calculate Gain Ratio

As we discussed in one of our article about How and when does the Decision tree stop splitting? Gain Ratio is modification of information gain that reduces its bias. Gain ratio overcomes the problem with information gain by taking into account the number of branches that would result before making the split.It corrects information gainContinue reading “How to calculate Gain Ratio”

How to calculate area using left and right Z table

We need to find the left area of the Z score 0.70 ,see the below.screenshot for more idea First we will find area by using below left Z table. Step 1: If we have any second level decimal values like 0.71 then we need to split in to 0.7 and 0.01, see the above tableContinue reading “How to calculate area using left and right Z table”

Z table transformation

We will use below table to calculate our probability. Fig 1: Lets solve some of the below problems to get more idea on transformation. What is the probability of P(z<=1.5) We need to find out the below highlighted area. To find the answer using the Z-table(see the Fig 1:), find whereContinue reading “Z table transformation”

Types of regression

When I ask what are the types of regression for the beginners, the expected answer will be linear regression and Logistic regression, because these are the two algorithms that all beginners will start with. Now coming to the point we have five types of regression. Linear regression Logistic regression Polynomial regression Ridge Regression Lasso RegressionContinue reading “Types of regression”

Classification Accuracy,Confusion matrix, AUC and ROC

we already discussed how to calculate accuracy for linear regression with the help of R-Square, Adjusted R-Square, MSE etc..Can we use the same mechanism to calculate the accuracy for classification problem? Simple answer is NO, we have different mechanism to calculate accuracy for classification problems. Classification Accuracy: It is very simple to calculate, number ofContinue reading “Classification Accuracy,Confusion matrix, AUC and ROC”

Range, Interquartile Range and Percentiles

Range(measures of variability): The range of a set of data is the difference between the largest and smallest values. Range might affect with Outliers like Mean. Interquartile Range(measures of variability) Interquartile range gives another measure of variability like Variance, Standard deviation. It is a better measure of dispersion than range because it leaves out the extremeContinue reading “Range, Interquartile Range and Percentiles”