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Questions and Answers
What is the purpose of model selection in machine learning?
Why does both validation error and testing error increase as the validation set increases?
In machine learning, what distinguishes supervised learning from unsupervised learning?
What is the main process involved in K-means clustering?
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What is the primary goal of unsupervised tasks in machine learning?
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Why are flat and hierarchical algorithms typically used in clustering?
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What is the purpose of re-assigning the clusters centroids positions in K-means clustering?
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What is the mathematical formula used to re-assign new centroids to new positions in K-means clustering?
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What is the key factor determining the termination conditions in K-means clustering?
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How does K-means convergence relate to the Expectation Maximization (EM) algorithm?
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What effect does an increase in the number of members in a cluster have on recomputation during K-means clustering?
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What does monotonic decrease in each Gk indicate during recomputation in K-means clustering?
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What does Σ(di - a)² reaching minimum imply during recomputation in K-means clustering?
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What is the primary difference between validation error and testing error in machine learning?
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Why does both validation error and testing error increase as the validation set increases?
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What distinguishes supervised learning from unsupervised learning in machine learning?
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Study Notes
Model Selection
- The purpose of model selection in machine learning is to choose the best model for a given problem.
Clustering
- K-means clustering is a type of unsupervised learning algorithm.
- The main process involved in K-means clustering is:
- Initialize centroids randomly
- Assign data points to the nearest centroid
- Re-assign centroids to the mean of their assigned data points
- Repeat until convergence
Unsupervised Learning
- The primary goal of unsupervised tasks in machine learning is to identify patterns or structure in the data.
Supervised vs Unsupervised Learning
- Supervised learning involves training a model on labeled data to make predictions on new data.
- Unsupervised learning involves training a model on unlabeled data to discover patterns or structure.
Clustering Algorithms
- Flat and hierarchical algorithms are typically used in clustering because they can handle large datasets and identify complex relationships.
K-means Clustering
- The purpose of re-assigning the clusters' centroids positions in K-means clustering is to minimize the sum of squared distances between data points and their assigned centroids.
- The mathematical formula used to re-assign new centroids to new positions in K-means clustering is the mean of all data points assigned to each centroid.
- The key factor determining the termination conditions in K-means clustering is the convergence of the centroids.
- K-means clustering is related to the Expectation Maximization (EM) algorithm because both algorithms involve iterative refinement of parameters to maximize the likelihood of the data.
K-means Convergence
- An increase in the number of members in a cluster slows down recomputation during K-means clustering.
- A monotonic decrease in each Gk during recomputation indicates convergence.
- Σ(di - a)² reaching minimum implies that the centroids have converged.
Model Evaluation
- Validation error and testing error increase as the validation set increases because the model is overfitting to the validation set.
- The primary difference between validation error and testing error is that validation error is used to tune hyperparameters, while testing error is used to evaluate the model's performance on unseen data.
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Description
This quiz covers the fundamental concepts of machine learning, including the machine learning process, unseen data, training set, ML algorithm, ML model (hypothesis), prediction process, overfitting, underfitting, and model selection. It also delves into the significance of model selection in finding the best model or hypothesis and optimizing hyperparameters.
