Big data computing week 6 quiz

Which of the following tasks can be best solved using Clustering ?

• Predicting the amount of rainfall based on various cues

• Detecting fraudulent credit card transactions

• Training a robot to solve a maze

• All of the mentioned

Identify the correct statement in context of Regressive model of Machine Learning.

• Regressive model organizes similar item in your dataset into groups.

• Regressive model predicts a numeric value instead of category.

• Regressive model comes up with a set of rules to capture associations between items

  or events.

• None of the Mentioned

_____refers to a model that can neither model the training data nor generalize to new data.

• Good fitting

• Overfitting

• Underfitting

• All of the mentioned

Which of the following is required by K-means clustering ?

• Defined distance metric

• Number of clusters

• Initial guess as to cluster centroids

• All of the mentioned

Imagine you are working on a project which is a binary classification problem. You trained a model on training dataset and get the below confusion matrix on validation dataset

• 1 and 3

• 1 and 4

• 2 and 3

• 2 and 4

Identify the correct method for choosing the value of k' in k-means algorithm ?

• Dimensionality reduction

• Elbow method

• Both Dimensionality reduction and Elbow method

• Data partitioning

True or False ?

If your model has very low training error but high generalization error, then it is overfitting.

• True

• False

Identify the correct statement(s) in context of overfitting in decision trees:

Statement I; The idea of Post-pruning is to grow a tree to its maximum size and then remove the nodes using a top-bottom approach.

Statement II: The idea of Pre-pruning is to stop tree induction before a fully grown tree is built, that perfectly fits the training data.

• Only Statement I is true

• Only Statement II is true

• Both Statements are true

• Both Statements are false

Which of the following options is/are true for K-fold cross-validation ?

I. Increase in K will result in higher time required to cross validate the result. 2. Higher values of K will result in higher confidence on the cross-validation result as

compared to lower value of K. 3. If K=N, then it is called Leave one out cross validation, where N is the number of observations.

• 1 and 2

• 2 and 3

• 1 and

• 1,2 and 3

Identify the correct statement(s) in context of machine learning approaches:

Statement I: In supervised approaches, the target that the model is predicting is unknown or unavailable. This means that you have unlabeled data.

Statement II: In unsupervised approaches the target, which is what the model is predicting, is provided. This is referred to as having labeled data because the target is labeled for every

sample that you have in your data set.

• Only Statement I is true

• Only Statement II is true

• Both Statements are false

• Both Statements are true