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Questions and Answers
What is the primary purpose of machine learning algorithms?
What is the primary purpose of machine learning algorithms?
Which of the following is NOT a type of machine learning?
Which of the following is NOT a type of machine learning?
How does supervised machine learning operate?
How does supervised machine learning operate?
Which of these applications would best illustrate unsupervised learning?
Which of these applications would best illustrate unsupervised learning?
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What is the bias-variance trade-off in machine learning?
What is the bias-variance trade-off in machine learning?
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In what year was the term 'machine learning' first used, and by whom?
In what year was the term 'machine learning' first used, and by whom?
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What role does the confusion matrix play in machine learning?
What role does the confusion matrix play in machine learning?
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Which of the following scenarios best exemplifies reinforcement learning?
Which of the following scenarios best exemplifies reinforcement learning?
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What is the definition of joint probability?
What is the definition of joint probability?
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How is the joint probability of two independent events A and B calculated?
How is the joint probability of two independent events A and B calculated?
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Which of the following pairs of events is considered independent?
Which of the following pairs of events is considered independent?
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If P(Red ∩ Dog) = 0.05, what does this represent?
If P(Red ∩ Dog) = 0.05, what does this represent?
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Calculate P(H1 ∩ H2), the probability of getting heads on both flips.
Calculate P(H1 ∩ H2), the probability of getting heads on both flips.
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For dependent events, how is joint probability represented?
For dependent events, how is joint probability represented?
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What is P(B|A) in the context of joint probability for dependent events?
What is P(B|A) in the context of joint probability for dependent events?
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Given P(smoker) = 0.2 and P(obese) = 0.3, what is the joint probability if the events are independent?
Given P(smoker) = 0.2 and P(obese) = 0.3, what is the joint probability if the events are independent?
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What is the primary purpose of Linear Discriminant Analysis (LDA) in machine learning?
What is the primary purpose of Linear Discriminant Analysis (LDA) in machine learning?
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What does False Positive (FP) signify in classification metrics?
What does False Positive (FP) signify in classification metrics?
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Which two criteria does LDA use to create a new axis?
Which two criteria does LDA use to create a new axis?
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In the context of the confusion matrix, what is True Negative (TN)?
In the context of the confusion matrix, what is True Negative (TN)?
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In what scenario does LDA especially excel compared to Logistic Regression?
In what scenario does LDA especially excel compared to Logistic Regression?
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When calculating the accuracy of a model, what is the formula used?
When calculating the accuracy of a model, what is the formula used?
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What type of data pre-processing can LDA perform?
What type of data pre-processing can LDA perform?
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Why is LDA considered useful in face detection algorithms?
Why is LDA considered useful in face detection algorithms?
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For the Setosa class, what are the True Positive (TP) and False Negative (FN) values?
For the Setosa class, what are the True Positive (TP) and False Negative (FN) values?
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What is indicated by a high value of True Positives (TP)?
What is indicated by a high value of True Positives (TP)?
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How does LDA achieve improved class separability?
How does LDA achieve improved class separability?
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Which of the following is NOT a benefit of using LDA?
Which of the following is NOT a benefit of using LDA?
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Given the model classified 100 tumors and the TN count is 90, what can be concluded?
Given the model classified 100 tumors and the TN count is 90, what can be concluded?
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What is a critical limitation of accuracy as a metric in model evaluation?
What is a critical limitation of accuracy as a metric in model evaluation?
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What dimensionality reduction is achieved by applying LDA to a 2-D dataset?
What dimensionality reduction is achieved by applying LDA to a 2-D dataset?
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For the Versicolor class, what are the False Positive (FP) and True Negative (TN) values?
For the Versicolor class, what are the False Positive (FP) and True Negative (TN) values?
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What condition causes Linear Discriminant Analysis (LDA) to fail?
What condition causes Linear Discriminant Analysis (LDA) to fail?
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In which application is Linear Discriminant Analysis primarily used?
In which application is Linear Discriminant Analysis primarily used?
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Which analysis technique reduces the dimensionality while retaining maximum information from the dataset?
Which analysis technique reduces the dimensionality while retaining maximum information from the dataset?
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What is the primary function of LDA in face recognition?
What is the primary function of LDA in face recognition?
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Which matrix is NOT involved in the calculations of LDA?
Which matrix is NOT involved in the calculations of LDA?
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What does LDA accomplish in the context of supervised classification?
What does LDA accomplish in the context of supervised classification?
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What is the output of the new features obtained from Principal Component Analysis called?
What is the output of the new features obtained from Principal Component Analysis called?
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When dealing with linear separability, which approach is recommended when LDA encounters shared means?
When dealing with linear separability, which approach is recommended when LDA encounters shared means?
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Study Notes
Introduction to Machine Learning
- Machine learning enables machines to learn from experiences and past data, similar to human learning.
- Arthur Samuel coined the term "machine learning" in 1959; it is a subfield of artificial intelligence (AI).
- Algorithms trained on datasets create self-learning models for predicting outcomes and classifying data without human intervention.
- Machine learning applications include recommendation engines, speech recognition, fraud detection, and self-driving vehicle features.
Types of Machine Learning
- Machine learning is categorized into three main types:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised Machine Learning
- Analogous to a teacher guiding students, it uses labeled data for training.
- Key performance metrics include:
- True Positive (TP): Correctly predicted positives.
- False Positive (FP): Incorrectly predicted positives (Type 1 Error).
- False Negative (FN): Incorrectly predicted negatives (Type 2 Error).
- True Negative (TN): Correctly predicted negatives.
- Confusion Matrix: A table to visualize performance across classes for classification tasks.
Accuracy Metrics
- Accuracy = (TP + TN) / Total Predictions.
- High accuracy does not suffice for imbalanced datasets; alternative metrics may be needed.
Probability in Machine Learning
- Probability denotes the likelihood of events; denoted as P(X).
- Joint Probability: P(A ∩ B) = P(A) * P(B) for independent events.
- Joint probability for dependent events: P(A ∩ B) = P(A) * P(B|A).
Dimensionality Reduction Techniques
-
Linear Discriminant Analysis (LDA):
- Transforms multiple dimensions into a lower-dimensional space to maximize class separability.
- Prioritizes maximizing the distance between class means and minimizing variance within classes.
- Useful for face detection and medical classification.
-
Principal Component Analysis (PCA):
- Maps higher-dimensional data to a lower-dimensional space while preserving variance.
- Aims to retain maximum information from the original dataset with minimal correlation among new components.
Applications of LDA
- Widely used in face recognition to reduce the number of features before classification.
- Assists in medical diagnosis classification based on patient data to inform treatment plans.
Summary of Analysis Steps for LDA
- Step 1: Calculate means for each class.
- Step 2: Compute covariance matrices for classes.
- Step 3: Determine within-class scatter matrix.
- Step 4: Calculate between-class scatter matrix.
Remember: LDA is effective for supervised classification tasks, particularly when classes are well-separated but may fail with overlapping distributions.
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Description
This quiz covers the foundational concepts of machine learning, including types of machine learning, bias-variance tradeoff, and evaluation metrics such as confusion matrix and accuracy. It also delves into dimensionality reduction techniques like PCA and LDA, critical for processing data effectively.