What best describes Supervised Learning?

• Supervised learning is a type of machine learning where inferences are drawn from datasets containing input data without labelled responses

• Supervised learning is the process by which multiple models, such as classifiers, are strategically generated and combined to solve a particular problem

• Supervised learning is a type of machine learning where a function is inferred from labelled training data

• Supervised learning is defined as a Machine Learning method that is concerned with how software agents should take actions in an environment

Please explain the goal of A/B Testing. Choose all correct answers

• A/B Testing is a statistical hypothesis testing meant for a randomized experiment with two variables, A and B.

• A/B Testing is utilized to optimize the recall value of the model

• A/B Testing is used to draw a conclusion whether a model is accurate or not

• A/B Testing is a methodology to determine which given 2 models result the most efficient outcome

Choose all algorithm that is considered as supervised learning

• Support Vector Machines

• K-means clustering

• Linear Regression

• Neural Networks

• Apriori Association

Suppose you are working on weather prediction, and you would like to predict whether or not it will be raining at 5pm tomorrow. You want to use a learning algorithm for this. Would you treat this as?

• Association

• Classification

• Clustering

• Regression

Suppose you are working on stock market prediction, Typically tens of millions of shares of Microsoft stock are traded (i.e., bought/sold) each day. You would like to predict the number of Microsoft shares that will be traded tomorrow. Would you treat this as?

• Association

• Classification

• Clustering

• Regression

What do you understand by the Selection Bias?

• Selection bias occurs when the person performing the data analysis wants to prove a predetermined assumption

• Selection bias is typically associated with research that doesn’t have a random selection of participants.

• Selection bias happens when there are extreme discrepancy among the data value

• Selection bias is the moment when the model gives an oversimplistic or overcomplicated picture of reality.

What are the differences between over-fitting and under-fitting? Choose all correct answers

• A statistical model describes random error or noise instead of the underlying relationship is called over-fitting.

• A statistical model describes random error or noise instead of the underlying relationship is called under-fitting.

• Overfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data

• Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data

What are Activation Functions in a Neural Network? Choose all correct answers

• It finds the root mean square error of all inputted signal

• It performs complex computations in the hidden layers and then transfers the result to the output layer.

• It rescales the span of the input into range 0 to 1

• It converts the linear input signals of a node into non-linear output to facilitate the learning of high order polynomials

Which equation below describes the hyperbolic tangent function?

•  $f\left(x\right)=\frac{1}{1+e^{-x}}$  

•  $f\left(x_i\right)=\frac{\exp\left(x_{\left(i\right)}\right)}{\sum_j^{ }\exp\left(x_j\right)}$  

•  $f\left(x\right)=\frac{e^x-e^{-x}}{e^x+e^{-x}}$  

•  $f\left(x\right)\ =\ \max\left(0,\ x\right)$  

Which equation below describes the ReLU function?

•  $f\left(x\right)=\frac{1}{1+e^{-x}}$  

•  $f\left(x_i\right)=\frac{\exp\left(x_{\left(i\right)}\right)}{\sum_j^{ }\exp\left(x_j\right)}$  

•  $f\left(x\right)=\frac{e^x-e^{-x}}{e^x+e^{-x}}$  

•  $f\left(x\right)\ =\ \max\left(0,\ x\right)$