Machine Learning Concepts Quiz

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In which scenario would KNN be most appropriate to use?

In cases where irrelevant features can significantly affect accuracy

When the data has thousands of features and limited training data

For classification problems with manageable features (correct)

When rapid classification response times are required

What characterizes a leaf node in a decision tree?

A test on an attribute

The root node from which all branches extend

The outcome of a test on training examples

A class label or class label distribution (correct)

What is a disadvantage of KNN?

It lacks a straightforward implementation

It requires extensive training time

It is prone to overfitting with large datasets

It can be slow at query time or classification (correct)

Which statement about decision trees is true?

The ID3 algorithm is commonly used to create decision trees Signup and view all the answers

What is an important step in training a decision tree?

Creating an experience table with historical data Signup and view all the answers

What distinguishes supervised learning from unsupervised learning?

Supervised learning requires guidance or labeled data. Signup and view all the answers

Which of the following algorithms is NOT typically associated with supervised learning?

Clustering Algorithm Signup and view all the answers

In the context of machine learning, what does it mean for a model to 'learn from the past'?

The model analyzes historical data to make predictions. Signup and view all the answers

What is a key characteristic of reinforcement learning compared to supervised and unsupervised learning?

Reinforcement learning learns through rewards and punishments. Signup and view all the answers

What role does the training dataset play in supervised learning?

It is the foundational basis for training the machine. Signup and view all the answers

Which of the following best defines machine learning?

The application of AI that allows systems to learn without being specifically programmed. Signup and view all the answers

What is the primary objective of using supervised learning algorithms in practical applications?

To classify and make predictions based on new data. Signup and view all the answers

What approach does unsupervised learning primarily use?

Identifying relationships by clustering unlabelled data. Signup and view all the answers

What is the purpose of a goodness function in decision tree classification?

To evaluate the splitting attributes Signup and view all the answers

What is the effect of bottom-up tree pruning in decision tree classification?

It helps in reducing overfitting Signup and view all the answers

Which attribute was used first in the decision tree based on the test case provided?

Outlook Signup and view all the answers

In which scenario will the decision tree classify the outcome as 'No' based on the given structure?

Overcast, hot, normal, false Signup and view all the answers

What does partitioning the examples recursively in decision tree construction achieve?

Allows for better class separation Signup and view all the answers

Which of the following represents a case where the decision tree would suggest playing?

Sunny, hot, normal, true Signup and view all the answers

Which method is typically used in decision trees to evaluate available attributes?

Goodness functions Signup and view all the answers

How do training examples initially start during decision tree construction?

All examples are placed at the root Signup and view all the answers

What outcome occurs when the weather is sunny and the humidity is high, according to the decision tree?

James will not play tennis. Signup and view all the answers

What does each internal node in the decision tree represent?

An attribute being tested. Signup and view all the answers

In the provided dataset, under what condition will James definitely play tennis?

When overcast with mild temperature. Signup and view all the answers

What is indicated by a leaf node in the decision tree?

A yes or no prediction is made. Signup and view all the answers

What is the classification when the outlook is rainy, temperature is mild, and humidity is normal?

James will play tennis. Signup and view all the answers

Which combination of conditions leads to the conclusion that James will not play tennis?

Sunny, high humidity. Signup and view all the answers

What does the attribute 'Windy' contribute to the decision-making process?

It can influence whether James plays tennis under certain conditions. Signup and view all the answers

What is the role of the 'Humidity' attribute in predicting if James will play tennis?

It helps differentiate between playing and not playing. Signup and view all the answers

What fundamental method does reinforcement learning primarily utilize to improve its performance?

Trial-and-error Signup and view all the answers

Which type of data is used in supervised learning?

Labelled data Signup and view all the answers

In reinforcement learning, what does the agent primarily discover from its interactions with the environment?

Errors or rewards based on actions Signup and view all the answers

Which type of machine learning does not require any pre-defined data?

Unsupervised Learning Signup and view all the answers

What is a common application problem solved by supervised learning?

Classification or regression Signup and view all the answers

What key aspect distinguishes reinforcement learning from the other types of machine learning?

Relies on an agent interacting with the environment Signup and view all the answers

How does an agent in reinforcement learning receive feedback?

From errors or rewards for actions Signup and view all the answers

What defines the approach of reinforcement learning?

Following a trial-and-error method Signup and view all the answers

Which type of learning involves no supervision?

Reinforcement Learning Signup and view all the answers

What primary goal does unsupervised learning achieve?

Understand patterns and discover output Signup and view all the answers

What does the information gain measure in decision trees?

The decrease in entropy after a split Signup and view all the answers

What is NOT a part of calculating the information gain?

Finding the average of the values in the dataset Signup and view all the answers

Which attribute had the highest information gain based on the content?

Outlook Signup and view all the answers

How is the entropy of a subset calculated based on the given information?

Using the formula -p+ log2 p+ - p- log2 p- Signup and view all the answers

In calculating expected information for attribute subsets, which aspect is considered?

The weighted average of entropy across subsets Signup and view all the answers

Entropy value can only be zero if:

All outcomes are the same Signup and view all the answers

What attribute showed the least information gain?

Temperature Signup and view all the answers

Why can’t we use a simple average for information gain calculations?

It does not account for the size of subsets Signup and view all the answers

What does high entropy indicate about a subset?

High uncertainty in classification Signup and view all the answers

How do you determine the expected information for the attribute subsets?

Using the weighted sum of each subset's entropy Signup and view all the answers

What effect does a larger entropy value in a subset have on information gain?

Decreases information gain Signup and view all the answers

Entropy is often expressed mathematically according to which formula?

E = -∑ p log(p) Signup and view all the answers

When calculating the information gain, why is the formula structured as Entropy(S) - ∑ E(Sv)?

To evaluate the reduction in uncertainty from the original set Signup and view all the answers