Most Asked Machine Learning Interview Questions

Why is logistic regression a type of classification technique and not a regression? Name the function it is derived from?

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Since the target column is categorical, it uses linear regression to create an odd function that is wrapped with a log function to use regression as a classifier. Hence, it is a type of classification technique and not a regression. It is derived from cost function. Logistic regression is categorized as a classification technique rather … Read more

When does the linear regression line stop rotating or finds an optimal spot where it is fitted on data?=

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A place where the highest RSquared value is found, is the place where the line comes to rest. RSquared represents the amount of variance captured by the virtual linear regression line with respect to the total variance captured by the dataset. In the context of machine learning, particularly linear regression, the process of finding the … Read more

List all assumptions for data to be met before starting with linear regression

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Before starting linear regression, the assumptions to be met are as follow: Linear relationship Multivariate normality No or little multicollinearity No auto-correlation Homoscedasticity Before applying linear regression, it’s important to ensure that certain assumptions about the data are met to obtain reliable results. Here are the key assumptions: Linearity: The relationship between the independent variables … Read more

What is target imbalance? How do we fix it? A scenario where you have performed target imbalance on data. Which metrics and algorithms do you find suitable to input this data onto?

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If you have categorical variables as the target when you cluster them together or perform a frequency count on them if there are certain categories which are more in number as compared to others by a very significant number. This is known as the target imbalance. Example: Target column – 0,0,0,1,0,2,0,0,1,1 [0s: 60%, 1: 30%, … Read more

Differentiate between regression and classification.

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Regression and classification are categorized under the same umbrella of supervised machine learning. The main difference between them is that the output variable in the regression is numerical (or continuous) while that for classification is categorical (or discrete). Example: To predict the definite Temperature of a place is Regression problem whereas predicting whether the day … Read more