Podcast
Questions and Answers
Which of the following algorithms is used for classification tasks?
Which of the following algorithms is used for classification tasks?
- Decision Tree (correct)
- Gaussian Mixture
- K-means
- Linear Regression
What defines a regression problem in supervised learning?
What defines a regression problem in supervised learning?
- The output variable is a mix of categories.
- The output variable is solely based on classification.
- The output variable is a real value. (correct)
- The output variable is categorical.
Which statement about Logistic Regression is correct?
Which statement about Logistic Regression is correct?
- It predicts continuous values.
- It is primarily used for classification problems. (correct)
- It requires unsupervised learning.
- It is a regression algorithm.
In supervised learning, what is the purpose of the testing phase?
In supervised learning, what is the purpose of the testing phase?
Which method is NOT part of the supervised learning classification algorithms?
Which method is NOT part of the supervised learning classification algorithms?
What is the focus of clustering techniques in unsupervised learning?
What is the focus of clustering techniques in unsupervised learning?
What does the accuracy measure in the context of a supervised learning model?
What does the accuracy measure in the context of a supervised learning model?
Which of the following describes the purpose of association rules in unsupervised learning?
Which of the following describes the purpose of association rules in unsupervised learning?
Which of the following is a common supervised machine learning algorithm?
Which of the following is a common supervised machine learning algorithm?
What is a primary advantage of supervised learning?
What is a primary advantage of supervised learning?
What distinguishes clustering from association problems in unsupervised learning?
What distinguishes clustering from association problems in unsupervised learning?
Which of the following is a disadvantage of supervised learning?
Which of the following is a disadvantage of supervised learning?
Which of the following best describes unsupervised learning?
Which of the following best describes unsupervised learning?
What is a characteristic of K Nearest Neighbors (K-NN) in supervised learning?
What is a characteristic of K Nearest Neighbors (K-NN) in supervised learning?
What is a disadvantage of unsupervised learning?
What is a disadvantage of unsupervised learning?
Which algorithm is commonly used for clustering in unsupervised learning?
Which algorithm is commonly used for clustering in unsupervised learning?
Flashcards
Supervised Learning
Supervised Learning
Learning from labeled data, where the input data is paired with the desired output, allowing the algorithm to predict outputs for new input data.
Classification
Classification
Predicting categorical output (e.g., good/bad, spam/not spam); the output variable is discrete.
Regression
Regression
Predicting continuous output values (e.g., price, temperature); the output variable is continuous.
Decision Tree
Decision Tree
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Discriminant Analysis
Discriminant Analysis
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Logistic Regression
Logistic Regression
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Naive Bayes
Naive Bayes
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Support Vector Machine (SVM)
Support Vector Machine (SVM)
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Linear Regression
Linear Regression
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SVR
SVR
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Regression Tree
Regression Tree
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Ensemble Methods
Ensemble Methods
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GLM
GLM
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Supervised Learning
Supervised Learning
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Unsupervised Learning
Unsupervised Learning
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Clustering
Clustering
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Association Rule Learning
Association Rule Learning
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Decision Trees
Decision Trees
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K-Nearest Neighbors (K-NN)
K-Nearest Neighbors (K-NN)
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Linear SVC
Linear SVC
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Logistic Regression
Logistic Regression
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Linear Regression
Linear Regression
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K-means Clustering
K-means Clustering
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Dimensionality Reduction
Dimensionality Reduction
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Hierarchical Clustering
Hierarchical Clustering
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Study Notes
Artificial Intelligence
- Artificial intelligence is a broad field encompassing machine learning, including supervised and unsupervised techniques.
Agenda
- The agenda covers machine learning and its applications, including supervised and unsupervised learning.
Supervised Learning Algorithms
- Classification: Categorizes data.
- Examples: Decision Tree, Discriminant Analysis, Naive Bayes, Logistic Regression, Support Vector Machine.
- Regression: Predicts continuous values.
- Examples: Linear Regression, SVR, Regression Tree, Ensemble Methods, GLM (Generalized Linear Model).
- Logistic regression is not a regression algorithm, but handles categorical data.
Unsupervised Learning Algorithms
- Clustering: Groups similar data points. Includes methods like hierarchical clustering, K-means, hidden Markov models, gaussian mixture models, and fuzzy c-means.
- Association: Identifies relationships in data. Discovered using association rules. These rules show how items often occur together.
Machine Learning Process
- Training: Training data is used to create a model using a machine learning algorithm.
- Testing: Unseen data is fed to the model to test its accuracy.
- Evaluation: The model's performance is evaluated using metrics like accuracy.
Supervised Learning Process: Two Steps
- Learning (training): A model is created from training data.
- Testing: The model uses unseen test data to assess accuracy (correct classifications / total test cases).
Supervised Learning
- Classification: Involves categorical output variables (such as "red", "blue", "disease", "no disease").
- Regression: Involves real-valued output variables (such as dollars or weight).
Common Supervised Machine Learning Algorithms
- Decision Trees
- K-Nearest Neighbors
- Linear SVC (Support Vector Classifier)
- Logistic Regression
- Linear Regression
Advantages of Supervised Learning
- Precise label definitions (e.g. specifically define types of disease).
- Ability to choose number of classes needed.
- Accuracy in results.
- Input data labeled and well-understood.
Disadvantages of Supervised Learning
- Complexity of methods.
- Often requires significant computational time (to train algorithms).
- Difficulty predefining labels for dynamic datasets.
Unsupervised Learning
- Input data only, no output variable.
- Model data structure to learn insights.
- Data analysis without correct answers or predetermined outcomes.
Unsupervised Learning
- Discover insights in raw data through algorithms.
- Processing and interpretation of results.
Unsupervised Learning Problems
- Clustering: Grouping similar data points (e.g., customer segmentation).
- Association: Rules showing relationships between data items (e.g., people buying X tend to also buy Y).
Common Unsupervised Machine Learning Algorithms
- K-means clustering
- K-Nearest Neighbors
- Dimensionality Reduction
- Hierarchical clustering
Advantages of Unsupervised Learning
- Simpler compared to supervised learning.
- Easier to get unlabeled data.
- Real-time analysis and labeling possible
Disadvantages of Unsupervised Learning
- Limited definition precision for data/outputs.
- Less precise output accuracy.
- Output from analysis not easily certified/guaranteed.
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