Machine Learning Top 10 Interview Questions. 10 Important Questions to Ask in a Machine Learning Interview.
What is the difference between supervised and unsupervised machine learning, and how do you explain it?
We must supply labelled data in supervised machine learning algorithms, such as stock market price prediction, but we do not require labelled data in unsupervised machine learning algorithms, such as email categorization into spam and non-spam.
What is the difference between clustering using KNN and k.means?
K-Nearby Neighbours is a supervised machine learning technique that requires labelled data to be provided to the model, which subsequently classifies the points based on their distance from the nearest points.
K-Means clustering, on the other hand, is an unsupervised machine learning technique that requires us to feed the model with unlabeled data. This algorithm divides points into clusters based on the mean of the distances between them.
What’s the difference between regression and classification?
- Classification is used to create distinct outcomes, as well as to categorise data into particular categories.
- Classifying e-mails into spam and non-spam categories, for example.
- When dealing with continuous data, such as predicting stock values at a certain point in time, regression analysis is used.
How do you make sure your model isn’t too tight?
- Keep the model’s design basic. Consider fewer variables and parameters to decrease the noise in the model.
- K-folds cross validation and other cross-validation approaches help us keep overfitting under control.
- Regularization techniques like LASSO assist to avoid overfitting by punishing specific factors that are likely to cause it.
What do the terms “training set” and “test set” mean?
- The provided data set was divided into two sections: ‘Training Set’ and ‘Test Set.’
- The subset of the dataset used to train the model is referred to as the ‘training set.’
- The ‘testing set’ refers to the subset of the dataset that is utilised to put the trained model to the test.
What are the primary benefits of Navie Bayes?
When compared to other models like logistic regression, a Naive Bayes classifier converges relatively quickly. As a result, in the case of a naïve Bayes classifier, we require less training data.
Explain the concept of ensemble learning?
Many basic models, such as classifiers and regressors, are produced and merged in ensemble learning to improve outcomes. It’s what we utilise to make accurate and independent component classifiers. There are two types of ensemble methods: sequential and parallel.
Explain how machine learning uses dimension reduction?
The process of decreasing the size of the feature matrix is known as dimension reduction. By merging columns or eliminating unnecessary variables, we aim to minimise the number of columns in order to achieve a better feature set.
When your model has a low bias and a large variance, what should you do?
Low bias occurs when the model’s projected value is extremely close to the actual value. We can utilise bagging techniques like the random forest regressor in this situation.
- Distinguish between the random forest and the gradient boosting algorithms.
- Bagging techniques are used in Random Forest, whereas boosting techniques are used in GBM.
- Random forests are primarily used to minimise variance, whereas GBM is used to reduce both bias and variance in a model.
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