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Machine Learning
Machine Learning
Random Forest Algorithm
Ensemble learning method that builds multiple decision trees and combines their predictions.
XGBoost Algorithm
Gradient boosting framework known for high performance and accuracy in predictive modeling.
Feature Selection Process
Process of selecting a subset of relevant features for model construction.
Isolation Forest Algorithm
Unsupervised anomaly detection algorithm that identifies outliers by isolating observations in random decision trees.
Cross-Validation Method
Technique to evaluate machine learning models by splitting data into training and validation sets multiple times.
Gradient Boosting Algorithm
Ensemble machine learning technique that builds models sequentially to correct previous errors.
Logistic Regression Algorithm
Statistical model for binary classification that predicts the probability of an outcome.
SVM (Support Vector Machine) Algorithm
Classification algorithm that finds the optimal hyperplane to separate classes with maximum margin.
KNN (K-Nearest Neighbors) Algorithm
Classification algorithm that predicts based on the majority class of the K closest training examples.
Neural Network Algorithm
Machine learning model inspired by the brain, using interconnected layers of nodes to learn patterns.
ML Pipeline Process
Automated workflow for building, training, and evaluating machine learning models.
Target Variable Metric
The outcome variable that a machine learning model is trained to predict.
Task Type (ML) Metric
The type of machine learning problem: Classification (categories) or Regression (continuous values).
F1 Score Metric
Harmonic mean of precision and recall, balancing both for classification evaluation.
Accuracy Metric
Percentage of correct predictions out of all predictions made.
Precision Metric
Of all positive predictions, what proportion was actually positive.
Recall Metric
Of all actual positives, what proportion was correctly identified.
MAE (Mean Absolute Error) Abbreviation
Average of absolute differences between predictions and actual values.
MSE (Mean Squared Error) Abbreviation
Average of squared differences between predictions and actual values.
RMSE (Root Mean Squared Error) Abbreviation
Square root of MSE, in the same units as the target variable.
ROC-AUC Metric
Area Under the ROC Curve, measuring classifier performance across all thresholds.
Confusion Matrix Metric
Table showing counts of true positives, false positives, true negatives, and false negatives.
Feature Importance Metric
Ranking of how much each feature contributes to model predictions.
Hyperparameter Tuning Process
Process of finding optimal algorithm settings to maximize model performance.
Grid Search Method
Exhaustive search over specified parameter combinations to find optimal settings.
Random Search Method
Random sampling of hyperparameter combinations, often faster than grid search.