Pearson Science 7 Answers Pdf

[Francesca Cruiz]

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The skill test covers important data science topics, such as unsupervised and supervised learning, reinforcement learning, Bayes theorem, k-means clustering, recommender systems, linear regression, logistic regression, random forest, and more. It also quizzes you on Statistics concepts such as Normal distribution, p-value, etc.

A) All categories of the categorical variables are not present in the test dataset.

B) The frequency distribution of categories is different in the train compared to the test dataset.

C) Train and Test always have the same distribution.

D) Both A and B

E) None of these

A) X_projected_PCA will have interpretation in the nearest neighbor space.

B) X_projected_tSNE will have interpretation in the nearest neighbor space.

C) Both will have interpretation in the nearest neighbor space.

D) None of them will have interpretation in the nearest neighbor space.

t-SNE algorithm considers nearest neighbor points to reduce the dimensionality of the data. So, after using t-SNE, we can think that reduced dimensions will also have interpretation in the nearest neighbor space. But in the case of PCA, it is not the case.

In Image 1, features have a high positive correlation, whereas, in Image 2, there is a high negative correlation between the features. So in both images, the pair of features is an example of multicollinear features.

The majority class is observed 99% of the time in the training data. Your model has 99% accuracy after taking the predictions on the test set. Which of the following is true in such a case?

A larger k-value means less bias towards overestimating the truly expected error (as training folds will be closer to the total dataset) and higher running time (as you are getting closer to the limit case: Leave-One-Out CV). We also need to consider the variance between the k folds accuracy while selecting the k.

In the first step, you pass an observation (q1) in the black box algorithm, so this algorithm returns the nearest observation and its class.

In the second step, you go through the nearest observation from train data and again input the observation (q1). The black box algorithm will again return the nearest observation and its class.

You need to repeat this procedure k times.

In Leave-One-Out cross-validation, we will select (n-1) observations for training and 1 observation of validation. Consider each point as a cross-validation point and then find the 3 nearest points to this point. So if you repeat this procedure for all points, you will get the correct classification for all positive classes given in the above figure, but the negative classes will be misclassified. Hence you will get 80% accuracy.

You are using logistic regression with L1 regularization.

Where C is the regularization parameter, and w1 & w2 are the coefficients of x1 and x2.

Which of the following option is correct when you increase the value of C from zero to a very large value?

By looking at the image, we see that even by just using x2, we can efficiently perform classification. So at first, w1 will become 0. As the regularization parameter increases more, w2 will come closer and closer to 0.

You trained a model on the training dataset and got the below confusion matrix on the validation dataset.

Based on the above confusion matrix, choose which option(s) below will give you the correct predictions.

Due to some reason, we forgot to tag the C values with visualizations. In that case, which of the following option best explains the C values for the images below (1,2,3 left to right, so C values are C1 for image1, C2 for image2, and C3 for image3 ) in the case of an rbf kernel?

Penalty parameter C of the error term. It also controls the trade-off between smooth decision boundaries and classifying the training points correctly. For large values of C, the optimization will choose a smaller-margin hyperplane. Read more here.

I hope these questions and answers helped you test your knowledge and maybe learn a thing or two about Python, machine learning, and deep learning. You can find all the information about our upcoming skill tests and other events here. Machine learning questions.

Hope you like this article and getting understanding about the machine learning questions and machine learning exam questions and answers.You Will get a clear understanding about these questions and answers and it will make a better for learning the machine learning exam question and answers will help you to clear the interviews.

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