Artificial Intelligence

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Today's Interview QnAs Company Name - Cognizant Role - Data Scientist Q.1 Describe how Gradient Boosting works. A. Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero. Gradient boosting produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. Q.2 Describe the decision tree model. A. Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The leaves are the decisions or the final outcomes. A decision tree is a machine learning algorithm that partitions the data into subsets. Q.3 What is a neural network? A. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. They, also known as Artificial Neural Networks, are the subset of Deep Learning. Q.4 Explain the Bias-Variance Tradeoff A. The bias–variance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters. Q.5 What’s the difference between L1 and L2 regularization? A. The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically. ------------------------------------- Happy Learning 💙

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Artificial Intelligence India https://www.linkedin.com/company/artificial-intelligence-in

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CS50’s Introduction to Artificial Intelligence with Python Free course by Harvard University https://cs50.harvard.edu/ai/2020