11. What is Dependent and Independent Variables in Machine Learning?
Independent Variables (or features) are the input variables that are used to predict the outcome. They are the factors that influence or explain changes in the dependent variable. For example, in a dataset predicting house prices, features like square footage, number of bedrooms, and location would be independent variables.
Dependent Variable (or target) is the output variable that you want to predict or explain. It depends on the independent variables. Continuing with the house price example, the dependent variable would be the actual price of the house, which is influenced by the independent variables.
12. What is Algorithm and Model in Machine Learning?
Algorithm refers to a set of rules or procedures that a computer follows to perform a specific task or solve a problem. In the context of machine learning, algorithms are used to analyze data, learn from it, and make predictions or decisions. Examples include decision trees, neural networks, and support vector machines.
Model is the result of applying a machine learning algorithm to a dataset. It represents the learned patterns or relationships in the data. A model can make predictions or classifications based on new, unseen data. For example, a trained neural network that can classify images of cats and dogs is a model derived from a specific algorithm and dataset.
13 What is Training and Testing in Machine Learning?
Training is the process of teaching a model to recognize patterns in a dataset. During training, the model learns from a labeled dataset by adjusting its parameters to minimize error between its predictions and the actual labels. This phase involves feeding the model input data along with the corresponding outputs (labels).
Testing is the phase where the trained model is evaluated on a separate dataset that it hasn’t seen before (the testing set). This allows you to assess how well the model generalizes to new data and measures its performance, typically using metrics like accuracy, precision, recall, or F1 score. Testing helps ensure that the model is not just memorizing the training data but can make accurate predictions on unseen data.
Note: Other few evaluation metrics of regression and clustering are Mean Squared Error, RMSE, Mean Absolute Error, R-squared, Silhouette Score, Calinski Harabaz Index, Davies Bouldin index
14. What is the Simple Machine Learning Workflow?
Note: These steps are very minimal steps in Simple Machine Learning Workflow if your dataset is already preprocessed and ready for model building. There are many other steps involved in the Simple Machine Learning Workflow which we will learn in the future.
15. Types of Machine Learning?
The main types of Machine Learning are:
- Supervised Learning
- Unsupervised Learning
- Semi-Supervised Learning
- Reinforcement Learning
- Self-supervised learning
Happy Learning !!!