Season 4 #Spaic Machine learning Weekly Quiz

In a classification problem, the outputs are

• categorical or discrete

• numerical or continuous

In a regression problem, the output is _

• categorical or discrete

• numerical or continuous

Tags

CCSS.HSS.ID.C.9

What is the difference between K-means and KNN Algorithms?

• K-means is supervised while KNN is unsupervised

• K-Means is used for clustering while KNN is used for classification and regression

KNeighborsClassifier class can be imported as:

• from sklearn.ensemble import KNeighborsClassifier

• from sklearn.neighbors import KNeighborsClassifier

• from sklearn.tree import KNeighborsClassifier

• from sklearn import KNeighborsClassifier

Which of the following is FALSE about SVM?

• SVM has inbuilt L2 regularization capabilities

• SVM solves both classification and regression problems

• It does not require any feature scaling

• Choosing an appropriate Kernel function is difficult

Which of the following is FALSE about Entropy in context of Decision Tree?

• Entropy keeps on increasing as we keep splitting the nodes

• Entropy is calculated using Information Gain

• None of the above

Which of the following is FALSE about Random Forest and Adaboost?

• Random Forest aims to decrease variance and not bias

• Adaboost aims to decrease bias not variance

• Both Adaboost and Random Forest aim to decrease both bias and variance

• None of the above

Which of the following is TRUE about Random Forest and Adaboost?

• Order of trees matters in Random Forest

• Order of trees matters in Adaboost

• Order of trees does not matter in Random Forest and Adaboost

• None of the above

Which of the following is FALSE about Boosting?

• Each weak learner has equal say in the final decision

• Sequence in which each weak learner is created does not matter

• None of the above

Which of the following is TRUE about Reinforcement Learning?

• It uses rewards and penalty methods to train a model

• It is also known as a semi - supervised learning model

• Chess playing computer is a good example of reinforcement learning

• All of the above