Frequently Asked Machine Learning Interview Questions

What is Machine Learning?

Machine learning is an area of computer science and artificial intelligence (AI) that concentrates on using algorithms and data to mimic how humans learn, aiming to improve efficiency steadily.

What Is The Difference Between Supervised and Unsupervised Machine Learning?

We must give labeled data in supervised machine learning algorithms, such as stock market index forecasts. However, we do not need labeled training data in unsupervised machine learning algorithms, such as e-mail classification into non-spam and spam.

What Is The Difference Between KNN and K-Means Clustering?

K-Nearest Neighbours (KNN) is a supervised machine learning approach in which we must first feed the model with labeled training data, after which the models categorize the points based on their distance from the nearest points. On the other hand, K-Means clustering is an unsupervised machine learning algorithm that requires unlabeled input to classify points into clusters.

What Is The Difference Between Regression and Classification?

Classification is used to provide discrete outcomes, as well as to categorize data into specified categories. Categorizing e-mails into non-spam and spam categories, for example. Regression analysis is used when dealing with continuous variables, such as predicting stock values at a specific point in time.

How Can You Tell If Your Model Is Overfitting?

Keep the model’s design simple. Use fewer parameters and variables to minimize the error in the model. K-folds and other cross-validation approaches allow us to keep overfitting in control.

Regularization methods like LASSO prevent overfitting by reducing particular factors that are likely to cause it.

What are Test Set and Training Set?

The given data set was divided into two sections: ‘Test Set.’ and ‘Testing Set.’ The fraction of the dataset used to train the model is called the ‘training set.’ The dataset section that can be used to test the trained model is referred to as the ‘testing set.’

What are The Advantages of Naive Bayes?

A Naive Bayes classifier converges relatively rapidly when related to other models such as logistic regression. As a result, in the case of a naïve Bayes classifier, we require fewer training data.

What is Ensemble Learning?

Many base models, such as regressors and classifiers, are developed and integrated into ensemble learning to improve outcomes. It’s what we utilize to give precise and unbiased component classifiers. There are two types of ensemble methods: parallel and sequential.

Elaborate Dimension Reduction in Machine Learning

The procedure of lowering the dimension of the feature matrix is known as dimension reduction. By merging columns or deleting superfluous variables, we work to minimize the number of columns to acquire a better feature set.

When Your Model Has High Variance and Low Bias, What Should You Do?

Low bias occurs when the model’s projected value is close to the actual value. We can utilize bagging algorithms like random forest regressors in this situation.

What Is The Difference Between Random Forest and Gradient Boosting Algorithms?

Bagging techniques are used in Random Forest, whereas boosting methods are used in GBM. Random forests are primarily used to minimize variance, whereas GBM reduces variance and bias in a model.

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