11 Questions
What is the purpose of using the training set in supervised learning?
To learn how to classify patients where diagnosis is not known
What is the main objective of the breast cancer diagnosis problem?
To predict the diagnosis for patients where diagnosis is not known
What is the role of the test set in supervised learning?
To evaluate the performance of the model
What is the benefit of linear separation in the plot of training data?
It separates the two classes of diagnosis
What does the gray circle represent in the plot of training data?
The test set
What is the purpose of EDA in the dataset?
To prepare the data for modeling
What is the difference between malignant and benign tumors?
Malignant tumors are cancerous, while benign tumors are not
What is the benefit of using supervised learning in breast cancer diagnosis?
It enables the model to learn from labeled data
What is the purpose of the patient ID column in the dataset?
To store the patient's identification
What is the role of the model in breast cancer diagnosis?
To predict the diagnosis for patients
What is the purpose of the classification algorithm in breast cancer diagnosis?
To predict the diagnosis for patients
Study Notes
Machine Learning and Data Science Process
- Herbert Simon's definition of learning: "any process by which a system improves performance from experience"
- Two ways to improve system performance:
- Acquiring new knowledge (e.g. acquiring new facts)
- Adapting its behavior (e.g. solving problems more accurately)
Types of Machine Learning
- Supervised learning: uses labeled examples with direct feedback
- Unsupervised/clustering learning: no feedback, groups data into clusters
- Semi-supervised: combines supervised and unsupervised learning, with some labeled data and mostly unlabeled data
Supervised vs Unsupervised Learning
- Supervised learning: can separate data into two groups with labeled data
- Unsupervised learning: can separate data into two groups based on similarity/distance
- Semi-supervised learning: combines both approaches
Machine Learning Stages
- Hypothesis
- Data (training or learning)
- Testing or generalization
- Training: acquiring knowledge, skills, and competencies from examples/data
- Testing: evaluating the performance of the learned system on unseen data
Training and Testing
- Training requires scenarios or examples (data)
- Testing evaluates performance on unseen data
- Cross-validation methods for small data sets
- The more relevant data, the better
Defining the Learning Task
- Improve on task, T, with respect to performance metric, P, based on experience, E
- Examples:
- Recognizing hand-written words
- Driving on four-lane highways using vision sensors
- Categorizing email messages as spam or legitimate
Classification Examples
- Cancer diagnosis: classifying patients as malignant (M) or benign (B)
- Breast cancer diagnosis: using supervised learning to classify patients
- Linear separation line: separating the two classes (M and B)
Learn the basics of machine learning, including the definition of learning, types of machine learning, and how to improve system performance from experience. Discover supervised and unsupervised learning methods.
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