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WorksheetsUdacity ML ND Quiz
Total questions: 21
Worksheet time: 10mins
Select the True Statements
Machine learning takes input data and static rules to predict the output value
Traditional programming analyzes input data and comes up with rules and output data
Machine learning uses knowledge about input data and expected output to learn the rules for modelling data relationships
Traditional programming uses static rules to process input data to produce output
Machine learning is a subset of artificial intelligence
True
False
In tabular data, what does a row and column represent?
Row = feature, column = entry
Column = feature, row = entry
Which of the following yields discrete categorical outputs?
Logisitc Regression
Classification
Linear Regression
Feature Learning
High Bias Means
Underfitting
Overfitting
This picture shows a result of ...
Supervised Learning: Classification
Unsupervised Learning: PCA
Unsupervised Learning: Regression
Supervised Learning: Regression
Which of the following hyper parameter(s), when increased may cause random forest to over fit the data?
Number of Trees
Depth of Tree
Learning Rate
Number of Neurons
[True or False] LogLoss evaluation metric can have negative values.
True
False
Imagine, you are solving a classification problems with highly imbalanced class. The majority class is observed 99% of times in the training data.
Your model has 99% accuracy after taking the predictions on test data. Which of the following is true in such a case?
Accuracy metric is not a good idea for imbalanced class problems.
Accuracy metric is a good idea for imbalanced class problems.
Precision and recall metrics are good for imbalanced class problems.
Precision and recall metrics aren’t good for imbalanced class problems.
................. is a widely used and effective machine learning algorithm based on the idea of bagging.
Regression
Classification
Random Forest
Decission Trees
How can you handle missing or corrupted data in a dataset?
A - Drop missing rows or columns
B - Assign a unique category to missing values
C - Replace missing values with mean/median/mode
A & B
B & C
A & C
A & B & C
Machine learning algorithms build a model based on sample data, known as
Training Data
Transfer Data
Data Training
None of the above
Predictive models having target attribute having discrete values can be termed as
Regression models
Classification models
Which of the following technique represents data mining procedure that tailors models to work specifically for the given training data set only?
Generalization
Overfitting
The goal of the linear regression model is to create a model that ___________ the sum of squares of residuals or errors (SSE)
Minimizes
Maximizes
Which of the following is a hyperparameter ?
Lambda
(Regularization)
Alpha (Learning Rate)
W Or Theta (Weights)
Given the following, what do you think about your ML Model Performance?
- Training Accuracy: 73%
- Test Accuracy: 68%
Overfitting
Underfitting
Can't Decide
Logistic function is also termed as which of the following?
Sigma function
Sigmoid function
Propalistic Function
Given the following, what do you think about your ML Model Performance?
- Training Accuracy: 94%
- Test Accuracy: 80%
Human Accuracy Reference: 98%
Machine Accuracy Reference: 95%
Overfitting
Underfitting
Can't Decide
Which of the following correlation coefficient value indicates (at the best) that there is no relationship between two variables?
-0.9
-0.04
0.7
0.35
Many substances that can burn (such as gasoline and alcohol) have a chemical structure based on carbon atoms; for this reason, they are called hydrocarbons. A chemist wants to understand how the number of carbon atoms in a molecule affects how much energy is released when that molecule combusts (meaning that it is burned). The chemists obtain the dataset below. In the column on the right, “kJ/mol” is the unit measuring the amount of energy released. examples.
You would like to use linear regression (hθ(x)=θ_0+θ_1x) to estimate the amount of energy released (y) as a function of the number of carbon atoms (x). Which of the following do you think will be the values you obtain for θ_0 and θ_1? You should be able to select the right answer without implementing linear regression.
θ_0=−1780.0, θ_1=−530.9
θ_0=−569.6, θ_1=−530.9
θ_0=−1780.0, θ_1=530.9
θ_0=−569.6, θ_1=530.9
