Quiz-1

Which of the following are TRUE about Supervised Learning?

• Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.

• Supervised learning algorithms are trained using labeled data.

• In Supervised Learning, we normally have two sets of problems, Classification and Regression.

• Supervised learning requires minimum human annotated data

In visualization shown above, fit of three different models (in blue line) on same training data. What can you conclude from these visualizations?

1) The training error in first model is higher when compared to second and third model.

2) The best model for this regression problem is the last (third) model, because it has minimum training error.

3) The second model is more robust than first and third because it will perform better on unseen data.

4) The third model is overfitting data as compared to first and second model.

5) All models will perform same because we have not seen the test data.

• 1 and 3

• 1 and 2

• 1, 3 and 4

• Only 5

Compute the following from the confusion matrix: Sensitivity

• 95.23

• 91.78

• 85.91

• 87.51

Suppose, you want to predict the class of new data point x=1 and y=1 using eucludian distance in 5-NN. In which class this data point belong to?

• + Class

• o Class

• Can’t say

• None of these

The linear regression equation is y = 61.93x - 1.79.  Use the equation to predict how far this person will travel after 10 hours of driving.

• 10 miles
• 617.5 miles
• 0.19 miles
• 500 miles

What type of correlation best describes the scatter plot shown?

• positive, weak

• positive, strong

• negative, weak

• no correlation

A restaurant sells pizza for the prices in the data table. Calculate the linear regression equation of the data.

• y = 1.5x + 12

• y = 12x + 1.5

• y = 0.67x - 8

• y = -8x + 0.67

How many output layers have the following Neural network?

• 1

• 2

• 3

• 4

Identify the following activation function :

φ(V) = Z + (1/ 1 + exp (– x * V + Y) ),

Z, X, Y are parameters

• Step function

• Ramp function

• Sigmoid function

• Gaussian function

A neuron with 3 inputs has the weight vector [0.2 -0.1 0.1]^T and a bias θ = 0. If the input vector is X = [0.2 0.4 0.2]^T then the total input to the neuron is:

• 0.20

• 1.0

• 0.02

• -1.0