Discover published sets by community

Explore tens of thousands of sets crafted by our community.

Categories

Computer Science

Machine Learning

Activation Functions in Neural Networks

Bias-Variance Tradeoff

Computer Vision Fundamental Concepts

Cross-validation Techniques

Data Preprocessing for Machine Learning

Deep Learning Fundamentals

Data Imbalance Handling

Dimensionality Reduction Methods

Ensemble Learning Techniques

Evaluation Metrics for Classification

Feature Engineering Techniques

Generative Models

Gradient Descent Variants

Hyperparameter Tuning

Machine Learning Best Practices

Loss Functions Explained

Machine Learning Security and Privacy

Machine Learning Pipelines

Machine Learning Frameworks

Model Explainability and Interpretability

Multiclass Classification Strategies

Natural Language Processing Terminology

Model Selection Criteria

Neural Network Architectures

Performance Optimization in Machine Learning

Time Series Analysis Techniques

Reinforcement Learning Concepts

Statistical Tests for Model Comparison

Evaluation Metrics for Classification

Flashcards

0/10

Still learning

Accuracy

The ratio of correctly predicted observations to the total observations. It is calculated as

Accuracy = \frac{TP+TN}{TP+FP+FN+TN}

where TP = True Positives, FP = False Positives, FN = False Negatives, and TN = True Negatives.

PR Curve (Precision-Recall Curve)

A graph showing the trade-off between precision and recall for different thresholds. A high area under the curve represents both high recall and high precision.

Log-Loss (Cross-Entropy Loss)

A performance metric that measures the penalty for incorrect predictions, where the penalty is logarithmically proportionate to the inverse of the predicted probability for the actual class. Calculated as

Log-Loss = -\frac{1}{N}\sum_{i=1}^{N}[y_i \log(p_i) + (1 - y_i)\log(1 - p_i)]

where

N

is the number of observations,

y_i

is the binary indicator of the class label, and

p_i

is the predicted probability.

Precision

The ratio of correctly predicted positive observations to the total predicted positive observations. Calculated as

Precision = \frac{TP}{TP + FP}

Confusion Matrix

A table used to describe the performance of a classification model, showing the actual and predicted classifications. It helps to visualize true positives, false positives, true negatives, and false negatives.

Recall

The ratio of correctly predicted positive observations to all observations in the actual class - true positive rate. Calculated as

Recall = \frac{TP}{TP + FN}

Receiver Operating Characteristic (ROC) Curve

A graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. The curve is generated by plotting the True Positive Rate (Recall) against the False Positive Rate at various threshold settings.

Area Under the Receiver Operating Characteristic Curve (AUC-ROC)

The area under the ROC curve. It provides an aggregate measure of performance across all possible classification thresholds. The value ranges from 0 to 1, where 1 implies a perfect model and 0.5 denotes a model with no discriminative ability.

F1 Score

The weighted average of Precision and Recall. Calculated as

F1 \ Score = 2 \times \frac{Precision \times Recall}{Precision + Recall}

Matthews Correlation Coefficient (MCC)

A coefficient that measures the quality of binary classifications, which generates a value between -1 and +1. A coefficient of +1 represents a perfect prediction, 0 is no better than random prediction, and -1 indicates total disagreement between prediction and observation. Calculated by the formula

MCC = \frac{TP \times TN - FP \times FN}{\sqrt{(TP + FP)(TP + FN)(TN + FP)(TN + FN)}}

Know

Still learning

Click to flip

Know