Blackboard Interview Questions

Here is the list Machine Learning Interview Questions which are recently asked in Blackboard company. These questions are included for both Freshers and Experienced professionals.

1. What is the difference between covariance and correlation?

“Covariance” indicates that the direction of the linear relationship between variables. “Correlation” on the other hand measures both the strength and direction of the linear relationship between two variables.

2. What is convex hull ?

Convex hulls have wide applications in the mathematics, statistics, combinatorial optimization, economics, geometric modeling, and ethology. Related structures include the orthogonal convex hull, convex layers, Delaunay triangulation and Voronoi diagram, and convex skull.

3. What cross validation technique would you use on time series data set? Is it k-fold or LOOCV?

K-fold cross-validation, for the time series data we utilize hold-out cross-validation where a subset of the data is reserved for validating the model performance.

4. What do you understand by Type I vs Type II error?

Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means the failing to reject the null hypothesis when it’s actually false.

5. When does regularization becomes necessary in Machine Learning?

This is a form of regression, that the constrains or shrinks the coefficient estimates towards zero. In other words, this technique discourages learning a more complex or flexible model, so as to avoid the risk of overfitting. A simple relation for linear regression looks like this.

6. What do you understand by Bias Variance trade off?

In statistics and machine learning, the bias–variance tradeoff is the property of a model that the variance of the parameter estimates across the samples can be reduced by increasing the bias in the estimated parameters.

7. What Is Bias and Variance in a Machine Learning Model?

Bias in Machine Learning is defined as the phenomena of observing results that are systematically prejudiced due to faulty assumptions. Variance is an error from sensitivity to small fluctuations in the training set. High variance can cause an algorithm to model the random noise in the training data, rather than the intended outputs . Variance is the difference between many model’s predictions.

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8. What Is the Trade-off Between Bias and Variance?

Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change the given different training data. Trade-off is tension between the error introduced by the bias and the variance.

9. Define Precision and Recall.

Text search on a set of documents, precision is the number of correct results divided by the number of all returned results. The measure is called precision at n or P@n. Precision is used with recall, the percent of all relevant documents that is returned by the search.

10. What Is Decision Tree Classification?

Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The tree can be explained by two entities, namely decision nodes and leaves.

11. What Is Pruning in Decision Trees, and How Is It Done?

Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances. A tree that is too large risks overfitting the training data and poorly generalizing to new samples.

12. Briefly Explain Logistic Regression.

Logistic regression is a statistical analysis method used to predict a data value based on prior observations of a data set. A logistic regression model predicts a dependent data variable by analyzing the relationship between one or more existing independent variables.

13. Explain the K Nearest Neighbor Algorithm.

K-Nearest Neighbor is one of the simplest Machine Learning algorithms based on Supervised Learning technique. K-NN algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categories.

14. What Is a Recommendation System?

A recommender system, or a recommendation system, is a subclass of information filtering system that seeks to predict the “rating” or “preference” a user would give to an item.

15. What Is Kernel SVM?

kernel is usually used to refer to the kernel trick, a method of using a linear classifier to solve a non-linear problem. The kernel function is what is applied on each data instance to map the original non-linear observations into a higher-dimensional space in which they become separable.

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Blackboard Interview Questions