Supervised learning Flashcards
Q: What does supervised learning refer to?
A: Algorithms that learn input (x) to output (y) mappings by being given examples to learn from, including the correct labels (y) for given inputs (x).
Q: How does a supervised learning algorithm learn?
A: By seeing correct pairs of input (x) and desired output label (y), the algorithm learns to predict the output (y) given new input (x).
Q: Give an example of supervised learning in email filtering.
A: Input: Email, Output: Spam or Not Spam (Spam Filter).
Q: Provide an example of supervised learning in speech recognition.
A: Input: Audio Clip, Output: Text Transcript.
Q: How is supervised learning used in machine translation?
A: Input: Text in one language (e.g., English), Output: Text in another language (e.g., Spanish).
Q: Describe the use of supervised learning in online advertising.
A: Input: Information about an ad and user, Output: Prediction of whether the user will click on the ad or not, which drives revenue for ad platforms.
Q: What is the role of supervised learning in self-driving cars?
A: Input: Image and sensor data, Output: Position of other cars to navigate safely.
Q: Explain how supervised learning is used in manufacturing.
A: Input: Picture of a manufactured product, Output: Detection of defects like scratches or dents (Visual Inspection).
Q: How does the housing price prediction example illustrate supervised learning?
A: By providing the learning algorithm with data pairs of house sizes and prices to predict the price of a new house based on its size.
Q: What is regression in the context of supervised learning?
A: Predicting a continuous number, such as house prices, from infinitely many possible values.
Q: What is the difference between fitting a straight line and a curve in supervised learning?
A: Fitting a straight line is a simpler model, whereas fitting a curve can capture more complex relationships between input (x) and output (y).
Q: What is the other major type of supervised learning problem apart from regression?
A: Classification, which involves predicting discrete labels such as spam or not spam.
Q: Why is supervised learning called “supervised”?
A: Because the learning algorithm is provided with a dataset that includes the correct labels for the input data, guiding the learning process.
Q: What do supervised learning algorithms learn to predict?
A: Input to output mappings (X to Y).
Q: What is a regression algorithm in supervised learning?
A: A type of supervised learning algorithm that predicts numbers from infinitely many possible numbers.
Q: What is a classification algorithm in supervised learning?
A: A type of supervised learning algorithm that predicts categories, which are a limited set of possible outputs.
Q: Give an example of a classification problem.
A: Breast cancer detection, where the goal is to classify a tumor as benign (0) or malignant (1).
Q: How can classification be visualized when predicting tumor malignancy?
A: By plotting the size of the tumor on the horizontal axis and using 0 or 1 on the vertical axis to represent benign or malignant tumors.
Q: What distinguishes classification from regression?
A: Classification predicts a small, finite set of possible categories, while regression predicts any number from an infinite set.
Q: Can classification problems have more than two categories?
A: Yes, for example, a cancer diagnosis could predict multiple types of cancer, resulting in categories like 0, 1, or 2.
Q: What terms are often used interchangeably in classification?
A: Output classes and output categories.
Q: How does classification handle non-numeric categories?
A: It can predict categories such as whether a picture is of a cat or a dog.
Q: How does the learning algorithm use multiple inputs for classification?
A: By finding a boundary that separates the different categories (e.g., benign vs. malignant tumors) based on multiple input features.
Q: What is the significance of the boundary line found by the learning algorithm in classification?
A: It helps classify new inputs, aiding in diagnosis or prediction.