Repeat the same procedure on every branch until the decision node of each branch is finalizedįor example, let's say you want to build a decision tree to decide whether you should accept or decline a job offer.

Choose the attribute with the highest information gain as the root node.Calculate your information gain of all attributes (we gain information on sorting different objects from each other).Calculate entropy of the target variable, as well as the predictor attributes.Explain the steps in making a decision tree. The formula and graph for the sigmoid function are as shown:ģ. The image shown below depicts how logistic regression works: Logistic regression measures the relationship between the dependent variable (our label of what we want to predict) and one or more independent variables (our features) by estimating probability using its underlying logistic function (sigmoid). The most commonly used unsupervised learning algorithms are k-means clustering, hierarchical clustering, and apriori algorithm.Unsupervised learning has no feedback mechanism.The most commonly used supervised learning algorithms are decision trees, logistic regression, and support vector machine.Supervised learning has a feedback mechanism.What are the differences between supervised and unsupervised learning? Here's a list of the most popular data science interview questions on the technical concept which you can expect to face, and how to frame your answers. Mention some techniques used for sampling.īasic and Advanced Data Science Interview Questions.How should you maintain a deployed model?.Differentiate between univariate, bivariate, and multivariate analysis.Explain the steps in making a decision tree.What are the differences between supervised and unsupervised learning?.Differentiate between Data Analytics and Data Science.10 Most Asked Data Science Interview Questions Simply, data science means analysing data for actionable insights. Data Science is simply the application of specific principles and analytic techniques to extract information from data used in strategic planning, decision making, etc. Data Science combines statistics, maths, specialised programs, artificial intelligence, machine learning etc.

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