The information that is used to randomly select a subset of data.

Information collected that can increase the level of certainty in a particular prediction.

The purity of each node in a decition tree.

To calculate a model’s accuracy using the test set, you pass the test set to your model to predict the class labels, and then compare the predicted values with actual values.

Unsupervised learning works on unlabelled data.Ĥ.

KNN takes a bunch of unlabelled points and uses them to predict unknown points.A high value of K in KNN creates a model that is over-fit.Supervised learning works on labelled data.K needs to be initialized in K-Nearest Neighbor.Which of the following statements are true? (Select all that apply.) When building a decision tree, we want to split the nodes in a way that increases entropy and decreases information gain.ģ. You can define Jaccard as the size of the intersection divided by the size of the union of two label sets.Ģ. Machine Learning with Python Final Exam Answersġ. In user-based approach, the recommendation is based on users of the same neighborhood, with whom he/she shares common preferences.In item-based approach, the recommendation is based on profile of a user that shows interest of the user on specific item.Which one is correct about user-based and item-based collaborative filtering? In content-based recommender systems, similarity of users should be measured based on the similarity of the actions of users.ģ.In content-based approach, the recommendation process is based on similarity of users.Content-based recommendation system tries to recommend items to the users based on their profile.Which one is TRUE about Content-based recommendation systems? Collaborative filtering is based on relationships between products and people’s rating patterns.Ģ. Module 5 – Dimensionality Reduction & Collaborative Filtering Answerġ. We can select the centroid through correlation analysis.We can randomly choose some observations out of the data set and use these observations as the initial means.

How is a center point (centroid) picked for each cluster in k-means? Customer Segmentation is a supervised way of clustering data, based on the similarity of customers to each other.ģ. As k-means is an iterative algorithm, it guarantees that it will always converge to the global optimum.Ģ.The objective of k-means, is to form clusters in such a way that similar samples go into a cluster, and dissimilar samples fall into different clusters.k-means divides the data into non-overlapping clusters without any cluster-internal structure.Which one is NOT TRUE about k-means clustering? When building a decision tree, we want to split the nodes in a way that decreases entropy and increases information gain.ġ. A classifier with lower log loss has better accuracy.ģ. K = 1) produces an overly generalized model.Ģ.

k = 100) produces a highly complex model, while a very low value of K (ex. K = 100) produces a model that is better than a very low value of K (ex. K = 100) produces an overly generalised model, while a very low value of k (ex. In K-Nearest Neighbors, which of the following is true: When there are multiple dependent variables.ġ.When we would like to identify the strength of the effect that the independent variables have on a dependent variable.When we should use Multiple Linear Regression? Which of the following matrices can be used to show the results of model accuracy evaluation or the model’s ability to correctly predict or separate the classes?ģ. Train and Test on the Same Dataset might have a high training accuracy, but its out-of-sample accuracy can be low.Ģ. A collection of numerical algorithms and domain-specific toolboxes.ġ.A library that provides high-performance, easy to use data structures.A popular plotting package that provides 2D plotting as well as 3D plotting.A collection of algorithms and tools for machine learning.A library for scientific and high-performance computation.Which of the following statements best describes the Python scikit library?

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