Semi-unsupervised Learning

Unsupervised Learning

Supervised Learning

Reinforcement Learning

Decision Trees

Rules in propotional Logic

Hidden-Markov Models (HMM)

Rules in first-order predicate logic

Linear Regression

Support Vector Machines

Neural Network

Case-based

Clustering

Classification

Classification and Regression

Dimensionality reduction

Regression

Deeper trees will always improve performance on the training data

Deeper trees will always improve performance when testing the model on unseen data

If a deeper tree improves performance on the training data, then it will also improve performance on new unseen data

Tossing a fair coin

Tossing a biased coin

Flow-Chart

Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label

Flow-Chart & Structure in which internal node represents test on a feature, each branch represents outcome of test and each leaf node represents class label

None of the mentioned

True

False

Running efficiently on large data bases.

Insert the vector down on each of the trees in the forest.

grows many classification trees

chooses the classification having the most votes over all the trees

from sklearn.ensemble import KNeighborsClassifier

from sklearn.neighbors import KNeighborsClassifier

from sklearn.tree import KNeighborsClassifier

from sklearn import KNeighborsClassifier

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

Tuples are immutable

Tuple items can be deleted using del keyword

Tuples can be declared as: x,y=1,2

None of the above

Random Forest aims to decrease both variance and bias

Random Forest aims to decrease variance not bias

Random Forest aims to decrease bias not variance

None of the above

Data Input and Output

Data Transformation

Model Training

None of the above