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WorksheetsML QUIZ1
Total questions: 25
Worksheet time: 18mins
In a linear regression problem, h(x) is the predicted value of the target variable, y is the actual value of the target variable, m is the number of training examples. What do we try to minimize?
(h(x) – y) / m
(h(x) – y)2 / 2*m
(h(x) – y) / 2*m
(y – h(x))
Can a cancer detection problem be solved by logistic regression?
In a logistic regression problem, what is a possible output for a new instance?
Let g be the sigmoid function. Let a = 0. What is the value of g(a)?
What is the Manhattan distance between a data point (9, 7) and a new query instance (3, 4)?
Which of the following statements is not true about the Decision tree?
It starts with a tree with a single leaf and assign this leaf a label according to a majority vote among all labels over the training set
It performs a series of iterations and on each iteration, it examine the effect of splitting a single leaf
It defines some gain mea sure that quantifies the improvement due to the split
Among all possible splits, it either choose the one that minimizes the gain and perform it, or choose not to split the leaf at all
Which of the following statements is not true about Information Gain?
It is a gain measure that is used in the ID3 algorithms
It is the difference between the entropy of the label before and after the split
It is based on the decrease in entropy after a data-set is split on an attribute
Constructing a decision tree is all about finding attribute that returns the lowest information gain
Given the entropy for a split, Esplit = 0.39 and the entropy before the split, Ebefore = 1. What is the Information Gain for the split?
Given entropy of parent = 1, weights averages = (3/4,1/4) and entropy of children = (0.9, 0). What is the information gain?
K-Nearest Neighbors (KNN) is classified as what type of machine learning algorithm?
Instance-based learning
Parametric learning
Model-based learning
Which of the following is not a supervised learning
PCA
NAIVE-BYES
DECISION TREE
LINEAR REGRESSION
The F1-score is the harmonic mean of which two classification metrics?
What is the range of the R-squared value in regression analysis?
-1 to 1
- infinity to 1
-1 to infinity
Which regression metric is most suitable when dealing with outliers in data?
Why is log loss (logarithmic loss) preferred in probabilistic classification models?
In regression models, what does a negative R-squared(coefficient of determination) indicate?
Precision is defined as:
What is the assumptions of Naïve Bayesian classifier?
Which of the following best describes the Bias-Variance Trade-off?
Increasing bias always reduces variance.
A model with zero bias always has zero variance.
As model complexity increases, bias decreases but variance increases.
Bias and variance are independent of each other.
In the context of Bias-Variance Trade-off, what happens when a model is too simple?
low bias, high variance
high bias, low variance
high bias, high variance
low bias, low variance
What is the primary purpose of pruning in a decision tree?
To ensure that the tree perfectly classifies the training data.
What is the purpose of using a validation set in addition to training and test sets?
To evaluate the final performance of the model.
To fine-tune hyperparameters and prevent overfitting
To fine-tune hyperparameters and prevent overfitting
To make the training process faster.
What is the main difference between pre-pruning and post-pruning?
Pre-pruning is applied after the tree is fully grown, while post-pruning is applied before growing the tree.
Pre-pruning stops tree growth early based on conditions, while post-pruning removes branches after the tree is fully grown.
Pre-pruning is always more effective than post-pruning.
Post-pruning does not affect overfitting.
How does L2 regularization (Ridge Regression) affect the model?
It shrinks some feature weights to exactly zero.
It reduces large coefficient values without setting them to zero
It increases model complexity by adding more features.
It removes irrelevant features from the dataset.
What happens when the regularization parameter (lambda) is set too high?
