Podcast
Questions and Answers
What is the primary purpose of regularization in machine learning?
What is the primary purpose of regularization in machine learning?
- To reduce overfitting (correct)
- To enhance training speed
- To improve data storage capacity
- To increase the model's complexity
What issue arises when a model overfits the data?
What issue arises when a model overfits the data?
- It performs well on unseen data
- It operates faster
- It generalizes effectively
- It memorizes the training data (correct)
Which of the following is a likely consequence of not employing regularization in a model?
Which of the following is a likely consequence of not employing regularization in a model?
- Faster convergence during training
- Increased risk of overfitting (correct)
- Reduced model interpretability
- Improved prediction accuracy on new data
How does regularization affect the training process of a model?
How does regularization affect the training process of a model?
Which of the following techniques is commonly used as a form of regularization?
Which of the following techniques is commonly used as a form of regularization?
What does the regularization rate 𝝀 specify during training?
What does the regularization rate 𝝀 specify during training?
How does raising the regularization rate 𝝀 affect overfitting?
How does raising the regularization rate 𝝀 affect overfitting?
What is a potential negative consequence of increasing the regularization rate 𝝀?
What is a potential negative consequence of increasing the regularization rate 𝝀?
Which statement is true regarding the regularization rate 𝝀?
Which statement is true regarding the regularization rate 𝝀?
Why might one choose to raise the regularization rate 𝝀 during model training?
Why might one choose to raise the regularization rate 𝝀 during model training?
What is the process called when the KNN algorithm estimates missing values in a dataset?
What is the process called when the KNN algorithm estimates missing values in a dataset?
Why is KNN particularly useful in handling datasets with missing values?
Why is KNN particularly useful in handling datasets with missing values?
Which of the following is NOT an application of KNN in machine learning?
Which of the following is NOT an application of KNN in machine learning?
What determines the recommendations a user receives?
What determines the recommendations a user receives?
In the context of KNN, what does data preprocessing typically involve?
In the context of KNN, what does data preprocessing typically involve?
Which statement correctly describes user assignment to groups?
Which statement correctly describes user assignment to groups?
How does KNN perform missing data imputation?
How does KNN perform missing data imputation?
What could be a consequence of poor group behavior for a user?
What could be a consequence of poor group behavior for a user?
Which of the following is least likely to influence a user's recommendations?
Which of the following is least likely to influence a user's recommendations?
How does user behavior impact the recommendation system?
How does user behavior impact the recommendation system?
What effect does reducing the regularization rate have on a model?
What effect does reducing the regularization rate have on a model?
What is the primary challenge that regularization seeks to address in machine learning?
What is the primary challenge that regularization seeks to address in machine learning?
Which of the following best describes overfitting?
Which of the following best describes overfitting?
What might be a consequence of omitting regularization in model training?
What might be a consequence of omitting regularization in model training?
How can regularization positively impact the performance of a machine learning model?
How can regularization positively impact the performance of a machine learning model?
What does Lasso (L1) primarily do in the context of feature selection?
What does Lasso (L1) primarily do in the context of feature selection?
How does Ridge (L2) differ from Lasso in feature selection?
How does Ridge (L2) differ from Lasso in feature selection?
Which statement best describes the concept of 'feature selection'?
Which statement best describes the concept of 'feature selection'?
What is a common misconception about Lasso and Ridge regression techniques?
What is a common misconception about Lasso and Ridge regression techniques?
Why is it important to avoid confusion between feature weighting and feature selection?
Why is it important to avoid confusion between feature weighting and feature selection?
Flashcards
Overfitting
Overfitting
A machine learning model learns the training data too well, including noise and outliers, leading to poor performance on unseen data.
Regularization
Regularization
Techniques used to prevent overfitting in machine learning models.
Regularization Rate
Regularization Rate
A value (𝝀) representing the strength of regularization during model training.
Effect of high 𝝀
Effect of high 𝝀
Hinder overfitting risks reducing the model's predictive power (increasing loss).
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Overfitting
Overfitting
Trying to solve a machine learning problem by creating a model that is too complex in relation to the available data.
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Regularization
Regularization
A technique in machine learning used to prevent overfitting, allowing a model to improve generalizations.
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Reducing Regularization Rate
Reducing Regularization Rate
Decreasing the strength of the regularization technique, leading to a higher risk of overfitting.
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KNN Missing Data Imputation
KNN Missing Data Imputation
KNN can estimate missing values in datasets by finding similar data points and averaging their values.
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User Group Assignment
User Group Assignment
A user is placed in a specific group based on their behavior.
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Group-Based Recommendation
Group-Based Recommendation
Recommendations are provided to a user based on the behavior of their assigned group.
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User Behavior
User Behavior
Activities and interactions of a user.
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Lasso Regression
Lasso Regression
A type of linear regression that adds a penalty to the loss function, shrinking some coefficients to zero.
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Ridge Regression
Ridge Regression
A type of linear regression that adds a penalty to the loss function, shrinking coefficients towards zero, but not to exactly zero.
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Feature Selection
Feature Selection
Identifying the most relevant features in a dataset for a machine learning model.
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Weight
Weight
A parameter in a linear model that represents the importance of a feature in predicting the output.
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Feature
Feature
An individual measurable property or characteristic of a phenomenon, observation, or object.
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Logistic Regression and KNN
- Logistic Regression predicts probabilities, not direct values
- Uses a sigmoid function (threshold 0.5). Values < 0.5 are 0, > 0.5 are 1
- z = b + W1X1 + W2X2 + ...+ WnXn (Linear Regression formula)
- Outcome (Y) is probability of outcome 1 (vs. outcome -1)
- Overfitting: a model that fits training data very well but poorly on new data.
- Regularization: reduces overfitting by creating simpler models. Decreases model's predictive power.
- L1 Regularization: Penalizes weights by absolute value; used for feature selection. Removes some completely.
- L2 Regularization: Penalizes weights by squared values; makes model more robust to outliers but doesn't remove features.
K-Nearest Neighbors (KNN)
- Supervised learning method: uses proximity to classify new data points
- Assumes similar points are near each other.
- Finds the K closest neighbours to a new data point
- Classifies new data based on the majority class among those nearest neighbours.
- K Value: Controls the number of neighbors considered. Odd values help avoid ties.
- Distance Metrics: Euclidean, Manhattan (city block), Minkowski, and Hamming.
- Euclidean treats distance in hyperspace.
- Manhattan (city block): absolute difference between each coordinate.
- Minkowski generalization for both.
- Hamming used with Boolean or strings, counts differences.
Cross-Validation
- Partitions data into k subsets (folds).
- Trains on k-1 folds, tests on remaining fold.
- Repeats k times.
- Measures average error across all k runs for more reliable evaluation.
- k-fold cross-validation is time consuming due to model retraining.
- Example datasets: 1000 observations, 5 folds. 800 for training, 200 for testing. Training & testing repeats 5 times to get average accuracy.
Applications
- Data preprocessing: Handles missing values by estimating them.
- Recommendation engines: Recommends content based on user behaviour & other similar users.
- Healthcare: Predicts risks (heart attack, prostate cancer) based on gene expressions.
- Pattern recognition: Identifies handwriting or text patterns.
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