INSE 6220 Week 11: Advanced Statistical Approaches to Machine Learning

Machine Learning Types and Concepts

• Unsupervised learning operates with only data without labels for patterns and structures identification.
• Classification deals with predicting discrete outcomes, whereas clustering groups data into clusters based on similarity without predefined labels.
• Reinforcement learning maximizes cumulative reward through trial-and-error interactions with an environment.
• Semi-supervised learning integrates both labeled and unlabeled data to improve model performance.

Classification Modeling

• The output variable in a classification predictive model is called the target variable or class label.
• The primary task of classification predictive modeling is to assign a class label to instances based on input features.
• Classification's main objective is to accurately categorize new observations based on learned patterns from training data.
• A feature in machine learning classification refers to an individual measurable property or characteristic of the input data.

Scikit-learn Methods

• The fit(X, y) method in scikit-learn trains a model using the features (X) and target (y) data.
• The predict(X) method makes predictions for new data based on the trained model.

K-NN Algorithm Details

• In the K-NN algorithm, a test point is classified by finding the majority class among its nearest neighbors.
• The K-NN algorithm's computation is most intensive during the classification phase when determining nearest neighbors.
• For real-valued data, a K-NN classifier averages the values of the closest neighbors to make predictions.
• For discrete data, K-NN assigns a class label based on the majority vote among the closest neighbors.

Evaluation and Validation

• The evaluation of a classification model assesses its performance, often using confusion matrices or metrics like accuracy and F1-score.
• K-fold cross-validation divides a dataset into k subsets, allowing for more reliable estimation of model performance by training on different segments.
• For a dataset of 100 rows divided into groups of roughly equal size in K-fold cross-validation, 10 folds create 10 subsets.

Algorithms in Classification

• Logistic Regression is primarily used for binary classification tasks to predict categorical outcomes.
• Support Vector Machine (SVM) performs classification by finding the hyperplane that maximizes the margin between different classes.
• A Naive Bayes classifier assumes independence between features to simplify model computations.

Ensemble and Other Methods

• In ensemble models, individual trees are built using different subsets of data and features, promoting diversity and improving overall performance.
• The logistic function in Logistic Regression transforms predicted values into probabilities for classification tasks.

PyCaret's Classification Module

• PyCaret's Classification Module can be applied to any classification problem, including binary and multiclass scenarios.
• The goal of this module is to streamline the creation and evaluation of classification models.
• A common use case includes automating feature engineering, model training, and hyperparameter tuning.
• PyCaret's setup function provides data preparation tools and integrates multiple preprocessing steps for efficient model development.

Test your knowledge on machine learning concepts, including supervised, unsupervised, semi-supervised, and reinforcement learning. Learn how to apply advanced statistical approaches to improve classification algorithms and make accurate predictions.