Mindtree – Machine Learning Interview Questions
Here is the list Machine Learning Interview Questions which are recently asked in Mindtree company. These questions are included for both Freshers and Experienced professionals.
1. Explain the difference between supervised and unsupervised machine learning?
Supervised learning algorithms are trained using labeled data. Unsupervised learning model finds the hidden patterns in data. In supervised learning, input data is provided to the model along side with the output. In unsupervised learning, only input data is provided to the model.
2. Explain the difference between KNN and k.means clustering?
KNN represents a supervised classification algorithm which will give new data points accordingly to the k number or the closest data points, while k-means clustering is an unsupervised clustering algorithm that gathers and groups data into k number of clusters.
3. What is the difference between classification and regression?
Classification is about predicting a label and regression is about predicting a quantity. That classification is that the problem of predicting a discrete class label output for an example. That regression is that the problem of predicting a continuous quantity output for an example.
4. How to ensure that your model is not overfitting?
To ensure that your model is not overfitting are:- 5. Keep the model simpler: remove some of the noise in the training data.
- Use cross-validation techniques such as k-folds cross-validation.
- Use regularization techniques such as LASSO.
5. What is meant by ‘Training set’ and ‘Test Set’?
Training data and test data sets are two different but important parts in machine learning. While training data is necessary to teach an ML algorithm, testing data, because the name suggests, helps you to validate the progress of the algorithm's training and adjust or optimize it for improved results.
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6. List the main advantage of Navie Bayes?
It doesn't require as much training data. It handles both continuous and discrete data. It is highly scalable with the number of predictors and data points. It is fast and may be used to make real-time predictions.
7. Explain Ensemble learning.
Ensemble learning is that the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the (classification, prediction, function approximation, etc.)
8. Explain dimension reduction in machine learning.
Dimensionality reduction refers to techniques for reducing the number of input variables in training data. Fewer input dimensions often mean correspondingly fewer parameters or a simpler structure within the machine learning model, referred to as degrees of freedom.
9. What should you do when your model is suffering from low bias and high variance? Explain differences between random forest and gradient boosting algorithm.
If we want to reduce the amount of variance in a prediction, we must add bias. Consider the case of a simple statistical estimate of a population parameter, such as estimating the mean from a little random sample of data. A single estimate of the mean will have high variance and low bias.
10. What is the "Curse of Dimensionality?"
Curse of Dimensionality refers to a set of problems that arise when working with high-dimensional data. A dataset with a large number of attributes, generally of the order of a hundred or more, is mentione to as high dimensional data.
11. Explain the Bias-Variance Tradeoff.
On the top left is the ground truth function f- the function we are trying to approximate. To fit a model we are only given two data points at a time. Even though f is not linear, given the limited amount of data, we decide to use linear models.
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