Introduction to Machine Learning - Workflow

Introduction to Machine Learning - Workflow

• Machine learning problems require a specific methodology
• Define the Problem: Crucial first step. Determine inputs, outputs, and the objective.
  • What is the main objective?
  • What is the input data? Is it available?
  • What type of problem (e.g., binary classification, clustering)?
  • What is the expected output?
• Collect Data: Essential for model development. The more and better data, the better the model performs. Data typically has a specific shape.
• Choose a Measure of Success: Define how success will be measured; e.g., precision, accuracy, customer retention, mean squared error (MSE) for regression, or precision, accuracy, and recall for classification.

Evaluation Protocol

• Hold-out Validation Set: Set aside a portion of the data as a test set. Train on the remaining data, tune parameters using the validation set, and finally evaluate on the test set.
• K-Fold Validation: Divide data into K partitions. Train on K-1 partitions and evaluate on the remaining partition. Repeat for each partition. The final score is the average of the K scores.
• Iterated K-Fold Validation with Shuffling: Apply K-fold validation multiple times with data shuffled between runs. Helps to make sure that the model generalizes well
• Data Representation: Data should accurately represent the problem. Avoid redundancies and temporal leaks.
• Avoid Duplicates: Remove duplicate data points to avoid inaccurate learning by the model.

Data Preparation

• Missing Data: Common problem in real-world data. Methods to handle:
  • Removing samples or features with missing values.
  • Imputing missing values (e.g., using the mean).
• Categorical Data: Ordinal or nominal.
  • Ordinal: Features that can be sorted (e.g., size).
  • Nominal: Features without inherent order (e.g., color).
• Feature Scaling: Important for many algorithms.
  • Normalization: Rescales features to a range of [0, 1].
  • Standardization: Centers features at mean 0 with standard deviation 1.
• Selecting Meaningful Features: Identify and remove redundant features to avoid overfitting. This can be done using methods like Principal Component Analysis (PCA).
• Splitting Data: Split data into subsets like training, testing, and validation sets. Testing set is used to evaluate overall performance, while validation helps tune the model's parameters.

This quiz explores the essential workflow of machine learning, covering steps like defining the problem, data collection, and evaluation protocols. It provides insights into methodologies such as hold-out and K-fold validation. Test your understanding of these critical concepts in machine learning!