Modeling in Machine Learning

A machine learning model is an abstraction derived from training data that can make predictions on new, unseen data. The modeling process involves training a machine learning algorithm on a dataset, tuning it to improve performance, validating it, and finally using it for predictions.

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Steps Involved in Modeling

The four basic steps in the machine learning modeling process are:

1. Choose a machine learning algorithm suitable for the problem and dataset.
2. Input the training dataset to help the algorithm learn and capture patterns.
3. Tune the parameters to improve learning and accuracy.
4. Evaluate the learned model on unseen data.

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Types of Parameters in Modeling

There are two types of parameters in machine learning:

• Model Parameters:
  • These are learned directly from the training data.
  • Example: Coefficients in linear regression, weights in neural networks, split attributes in decision trees.
• Hyperparameters:
  • These are high-level configuration settings not learned directly from the data.
  • Example: Learning rate, regularization term, number of decision trees in a random forest.

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Error and Loss Function

When predictions are made, errors can occur:

• Training Error (In-Sample Error):
  Error when predicting on the training dataset.
• Test Error (Out-of-Sample Error):
  Error when predicting on new, unseen data.
• Loss Function (Mean Squared Error – MSE):
  Measures the average squared difference between predicted and actual values:

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Model Selection and Model Evaluation

Two main concerns during model selection are:

1. Model Performance – How well it performs on training data.
2. Model Complexity – How complex the model becomes after training.

Model selection involves choosing the best-suited model or tuning hyperparameters. Since no single model is perfect, we aim to choose a model that performs reasonably well.

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Approaches for Model Selection

1. Resampling Methods (e.g., Train/Test Split, Cross-validation)
2. Direct Performance Measures (e.g., accuracy, error rate)
3. Scoring Methods (e.g., Minimum Description Length)

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Resampling Methods

Used to tune and evaluate the model using subsets of the dataset:

• Holdout Method:
  • Split the dataset into a training and testing set.
  • Simple but can suffer from high variance.
• K-Fold Cross Validation:
  • Split data into k folds.
  • Use k-1 folds to train and 1 fold to test, repeated k times.
  • Final score is the average of k test scores.
• Stratified K-Fold:
  • Like K-Fold, but ensures class balance is maintained in all folds.
• Leave-One-Out Cross Validation (LOOCV):
  • Each sample is tested once, using all other samples for training.
  • Very accurate but computationally expensive.

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Scoring-Based Model Selection: MDL Principle

• Minimum Description Length (MDL):
  Chooses the model that minimizes total encoding length of:
• The model
• The data given the model

• It supports Occam’s Razor, favoring simpler models with sufficient performance.