Advanced Machine Learning Interview Questions

29. Explain the working principle of SVM.

A data set that is not separable in different classes in one plane may be separable in another plane. This is exactly the idea behind the SVM in this a low dimensional data is mapped to high dimensional data so, that it becomes separable in the different classes. A hyperplane is determined after mapping the data into a higher dimension which can separate the data into categories. SVM model can even learn non-linear boundaries with the objective that there should be as much margin as possible between the categories in which the data has been categorized. To perform this mapping different types of kernels are used like radial basis kernel, gaussian kernel, polynomial kernel, and many others.

30. What is the difference between the k-means and k-means++ algorithms?

The only difference between the two is in the way centroids are initialized. In the k-means algorithm, the centroids are initialized randomly from the given points. There is a drawback in this method that sometimes this random initialization leads to non-optimized clusters due to maybe initialization of two clusters close to each other. 

To overcome this problem k-means++ algorithm was formed. In k-means++, The first centroid is selected randomly from the data points. The selection of subsequent centroids is based on their separation from the initial centroids. The probability of a point being selected as the next centroid is proportional to the squared distance between the point and the closest centroid that has already been selected. This guarantees that the centroids are evenly spread apart and lowers the possibility of convergence to less-than-ideal clusters. This helps the algorithm reach the global minima instead of getting stuck at some local minima. Read more about it here.

31. Explain some measures of similarity which are generally used in Machine learning.

Some of the most commonly used similarity measures are as follows:

• Cosine Similarity – By considering the two vectors in n – dimension we evaluate the cosine of the angle between the two. The range of this similarity measure varies from [-1, 1] where the value 1 represents that the two vectors are highly similar and -1 represents that the two vectors are completely different from each other.
• Euclidean or Manhattan Distance – These two values represent the distances between the two points in an n-dimensional plane. The only difference between the two is in the way the two are calculated.
• Jaccard Similarity – It is also known as IoU or Intersection over union it is widely used in the field of object detection to evaluate the overlap between the predicted bounding box and the ground truth bounding box.

32. What happens to the mean, median, and mode when your data distribution is right skewed and left skewed?

In the case of a left-skewed distribution also known as a positively skewed distribution mean is greater than the median which is greater than the mode. But in the case of left-skewed distribution, the scenario is completely reversed.

Right Skewed Distribution

Mode < Median < Mean

Left Skewed Distribution,

Mean <Median < Mode

33. Whether decision tree or random forest is more robust to the outliers.

Decision trees and random forests are both relatively robust to outliers. A random forest model is an ensemble of multiple decision trees so, the output of a random forest model is an aggregate of multiple decision trees.

So, when we average the results the chances of overfitting get reduced. Hence we can say that the random forest models are more robust to outliers.

34. What is the difference between L1 and L2 regularization? What is their significance?

L1 regularization: In L1 regularization also known as Lasso regularization in which we add the sum of absolute values of the weights of the model in the loss function. In L1 regularization weights for those features which are not at all important are penalized to zero so, in turn, we obtain feature selection by using the L1 regularization technique.

L2 regularization: In L2 regularization also known as Ridge regularization in which we add the square of the weights to the loss function. In both of these regularization methods, weights are penalized but there is a subtle difference between the objective they help to achieve. 

In L2 regularization the weights are not penalized to 0 but they are near zero for irrelevant features. It is often used to prevent overfitting by shrinking the weights towards zero, especially when there are many features and the data is noisy.

35. What is a radial basis function? Explain its use.

RBF (radial basis function) is a real-valued function used in machine learning whose value only depends upon the input and fixed point called the center. The formula for the radial basis function is as follows:

Machine learning systems frequently use the RBF function for a variety of functions, including:

• RBF networks can be used to approximate complex functions. By training the network’s weights to suit a set of input-output pairs, 
• RBF networks can be used for unsupervised learning to locate data groups. By treating the RBF centers as cluster centers,
• RBF networks can be used for classification tasks by training the network’s weights to divide inputs into groups based on how far from the RBF nodes they are.

It is one of the very famous kernels which is generally used in the SVM algorithm to map low dimensional data to a higher dimensional plane so, we can determine a boundary that can separate the classes in different regions of those planes with as much margin as possible. 

36. Explain SMOTE method used to handle data imbalance.

The synthetic Minority Oversampling Technique is one of the methods which is used to handle the data imbalance problem in the dataset. In this method, we synthesized new data points using the existing ones from the minority classes by using linear interpolation. The advantage of using this method is that the model does not get trained on the same data. But the disadvantage of using this method is that it adds undesired noise to the dataset and can lead to a negative effect on the model’s performance.

37. Does the accuracy score always a good metric to measure the performance of a classification model?

No, there are times when we train our model on an imbalanced dataset the accuracy score is not a good metric to measure the performance of the model. In such cases, we use precision and recall to measure the performance of a classification model. Also, f1-score is another metric that can be used to measure performance but in the end, f1-score is also calculated using precision and recall as the f1-score is nothing but the harmonic mean of the precision and recall. 

38. What is KNN Imputer?

We generally impute null values by the descriptive statistical measures of the data like mean, mode, or median but KNN Imputer is a more sophisticated method to fill the null values. A distance parameter is also used in this method which is also known as the k parameter. The work is somehow similar to the clustering algorithm. The missing value is imputed in reference to the neighborhood points of the missing values.

39. Explain the working procedure of the XGB model.

XGB model is an example of the ensemble technique of machine learning in this method weights are optimized in a sequential manner by passing them to the decision trees. After each pass, the weights become better and better as each tree tries to optimize the weights, and finally, we obtain the best weights for the problem at hand. Techniques like regularized gradient and mini-batch gradient descent have been used to implement this algorithm so, that it works in a very fast and optimized manner.

40. What is the purpose of splitting a given dataset into training and validation data?

The main purpose is to keep some data left over on which the model has not been trained so, that we can evaluate the performance of our machine learning model after training. Also, sometimes we use the validation dataset to choose among the multiple state-of-the-art machine learning models. Like we first train some models let’s say LogisticRegression, XGBoost, or any other than test their performance using validation data and choose the model which has less difference between the validation and the training accuracy.

41. Explain some methods to handle missing values in that data.

Some of the methods to handle missing values are as follows:

• Removing the rows with null values may lead to the loss of some important information.
• Removing the column having null values if it has very less valuable information. it may lead to the loss of some important information.
• Imputing null values with descriptive statistical measures like mean, mode, and median.
• Using methods like KNN Imputer to impute the null values in a more sophisticated way.

42. What is the difference between k-means and the KNN algorithm?

k-means algorithm is one of the popular unsupervised machine learning algorithms which is used for clustering purposes. But the KNN is a model which is generally used for the classification task and is a supervised machine learning algorithm. The k-means algorithm helps us to label the data by forming clusters within the dataset.

43. What is Linear Discriminant Analysis?

LDA is a supervised machine learning dimensionality reduction technique because it uses target variables also for dimensionality reduction. It is commonly used for classification problems. The LDA mainly works on two objectives:

• Maximize the distance between the means of the two classes.
• Minimize the variation within each class.

44. How can we visualize high-dimensional data in 2-d?

One of the most common and effective methods is by using the t-SNE algorithm which is a short form for t-Distributed Stochastic Neighbor Embedding. This algorithm uses some non-linear complex methods to reduce the dimensionality of the given data. We can also use PCA or LDA to convert n-dimensional data to 2 – dimensional so, that we can plot it to get visuals for better analysis. But the difference between the PCA and t-SNE is that the former tries to preserve the variance of the dataset but the t-SNE tries to preserve the local similarities in the dataset.

45. What is the reason behind the curse of dimensionality?

As the dimensionality of the input data increases the amount of data required to generalize or learn the patterns present in the data increases. For the model, it becomes difficult to identify the pattern for every feature from the limited number of datasets or we can say that the weights are not optimized properly due to the high dimensionality of the data and the limited number of examples used to train the model. Due to this after a certain threshold for the dimensionality of the input data, we have to face the curse of dimensionality.

46. Whether the metric MAE or MSE or RMSE is more robust to the outliers.

Out of the above three metrics, MAE is robust to the outliers as compared to the MSE or RMSE. The main reason behind this is because of Squaring the error values. In the case of an outlier, the error value is already high and then we squared it which results in an explosion in the error values more than expected and creates misleading results for the gradient.

47. Why removing highly correlated features are considered a good practice?

When two features are highly correlated, they may provide similar information to the model, which may cause overfitting. If there are highly correlated features in the dataset then they unnecessarily increase the dimensionality of the feature space and sometimes create the problem of the curse of dimensionality. If the dimensionality of the feature space is high then the model training may take more time than expected, it will increase the complexity of the model and chances of error. This somehow also helps us to achieve data compression as the features have been removed without much loss of data.

48. What is the difference between the content-based and collaborative filtering algorithms of recommendation systems?

In a content-based recommendation system, similarities in the content and services are evaluated, and then by using these similarity measures from past data we recommend products to the user. But on the other hand in collaborative filtering, we recommend content and services based on the preferences of similar users. For example, if one user has taken A and B services in past and a new user has taken service A then service A will be recommended to him based on the other user’s preferences.

Machine learning is a subfield of artificial intelligence that involves the development of algorithms and statistical models that enable computers to improve their performance in tasks through experience. So, Machine Learning is one of the booming careers in upcoming years.

If you are preparing for your next machine learning interview, this article is a one-stop destination for you. We will be discussing the top 45+ most frequently asked machine learning interview questions for 2024. Our focus will be on real-life situations and questions that are commonly asked by companies like Google, Microsoft and Amazon during their interviews.

In this article, we’ve covered a wide range of machine learning questions for both freshers and experienced individuals, ensuring thorough preparation for your next ML interview. This ML Questions is also beneficial for individuals who are looking for a quick revision of their machine-learning concepts.