Machine Learning Principles Overview

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Questions and Answers

What is unsupervised learning?

A type of machine learning where the model is trained on unlabelled data to discover hidden patterns.

What is supervised learning?

A type of machine learning where the model is trained on labelled data to map inputs to outputs.

What is regression in machine learning?

A predictive modeling task aimed at predicting a continuous outcome.

What is classification?

A task where the model assigns input data into discrete categories or classes. Signup and view all the answers

What is underfitting in machine learning?

When a model is too simple to capture the underlying pattern in the data. Signup and view all the answers

What is overfitting?

When a model performs well on training data but poorly on test data due to learning noise. Signup and view all the answers

What is model selection?

The process of selecting the most appropriate machine learning model for a given task. Signup and view all the answers

What is a training dataset?

The portion of data used to train the model to learn relationships between inputs and outputs. Signup and view all the answers

What is a validation dataset?

A separate set of data used to tune model parameters without training on it. Signup and view all the answers

What is a test dataset?

A dataset used to evaluate the performance of the trained model. Signup and view all the answers

What is cross-validation?

A technique for evaluating models by splitting the data into multiple folds for training and validation. Signup and view all the answers

What does the no free lunch theorem state?

No one machine learning algorithm works best for all problems. Signup and view all the answers

What are model parameters?

Internal variables of the model that are learned from the training data. Signup and view all the answers

What is a parametric model?

A model that assumes a specific form for the function mapping inputs to outputs with a fixed number of parameters. Signup and view all the answers

What is a nonparametric model?

A model that does not assume a fixed form for the function and can grow in complexity as more data is provided. Signup and view all the answers

What is a likelihood function?

A function that measures how likely it is that a given set of parameters explains the observed data. Signup and view all the answers

What is maximum likelihood estimation (MLE)?

A method for estimating model parameters by maximizing the likelihood function. Signup and view all the answers

Study Notes

Machine Learning Principles

Unsupervised Learning: Model learns from unlabelled data to find hidden patterns or structures. Example: K-means clustering algorithm groups similar data points together.
Supervised Learning: Model learns from labelled data, mapping inputs to outputs based on provided examples. Examples include linear regression (predicting continuous values), logistic regression (predicting categorical outcomes), and support vector machines (separating data points into classes).
Regression: Predicting a continuous outcome based on input variables.
Classification: Assigning input data into discrete categories or classes.
Underfitting: Model is too simple to capture the underlying patterns in data. The model performs poorly on both training and testing data. Solution: increase model complexity or add more features.
Overfitting: The model fits the training data too well, but performs poorly on test data since it learns noise and details specific to the training data. Solution: gather more data or apply cross-validation.
Model Selection: Choosing the best machine learning model for a specific task. This involves comparing models, evaluating performance, and selecting the one that generalises best to unseen data. Cross-validation is a key tool for model selection.
Training Dataset: Data used to train the model, allowing the model to determine relationships between inputs and outputs.
Validation Dataset: A separate set of data used to tune model parameters (hyperparameters). It helps prevent overfitting by evaluating how well the model generalises to unseen data.
Test Dataset: Data used to evaluate the performance of the trained model on previously unseen data.
Cross-Validation: A technique for evaluating a model by splitting the data into multiple folds. The model trains on (S-1) folds and validates on the remaining fold. This ensures the model generalises well to new data.
No Free Lunch Theorem: No single machine learning algorithm is the best for every problem. The best performing algorithm depends on the specific task and dataset.
Model Parameters: Internal variables of the model that are learned from the training data, such as weights. These parameters define the model's behavior and how it makes predictions.
Parametric Model: Assumes a specific form for the function mapping inputs to outputs and has a fixed number of parameters, regardless of the size of the dataset. Examples include linear regression and logistic regression.
Nonparametric Model: Does not assume a fixed form for the function and can grow in complexity as more data becomes available. It has a growing number of parameters as the data grows.
Likelihood Function: Measures how likely a given set of parameters explains the observed data.
Maximum Likelihood Estimation (MLE): A method for estimating the parameters of a model by maximizing the likelihood function.
Bias Term: A constant value added to the model to ensure it can fit datasets that don't form y = mx + b. It allows the model to capture variability in the data and potentially improve prediction accuracy.
Dummy Variable: A variable that converts a categorical feature into a numerical format. It is essential for models that require numerical inputs, transforming a categorical variable into a binary representation (e.g., 0 or 1) to represent the presence or absence of a particular characteristic.

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Description

This quiz covers fundamental principles of machine learning, including unsupervised and supervised learning, regression, and classification techniques. It also addresses common issues like underfitting and overfitting, helping you understand how models learn from data. Test your knowledge on essential concepts to enhance your understanding of machine learning.