Machine Learning Metrics and Model Evaluation

The process of assessing and improving a machine learning model's performance.

Quantifiable measures that describe how well a model performs.

Metrics used to evaluate the performance of regression models. Measuring how well a model predicts continuous values.

A metric that shows how much of the variation in the data is explained by the model.

A basic model that predicts the average of the observed response. It has an intercept but no slope.

A process where the model's performance is improved by adjusting its parameters.

The process of making a trained model ready for use in a real-world setting.

A systematic method for exploring and evaluating different hyperparameter combinations to find the best model configuration.

A table that summarizes classification model performance by showing the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN).

The ability of a model to correctly identify all relevant cases within a dataset.

The ability of a classification model to identify only relevant data points.

A metric that combines precision and recall into a single score, representing the harmonic mean of the two metrics.

The percentage of correct predictions made by a model divided by the total number of predictions.

The presence of imbalanced data can lead to misleading accuracy scores, as the model might favor the majority class due to its larger presence.

Understanding the types of errors (FP and FN) made by a model can provide insights into its weaknesses and help improve performance.

Refers to the ability of a classifier to correctly identify all actual positive instances. It addresses the question: 'When the class was actually TRUE, how often did the classifier get it right?'

The cost of a false positive is high when using precision. This is important in scenarios where misclassifying a negative instance as positive can have serious consequences.

The importance of recall is emphasized when missing a positive instance is significantly worse than misclassifying a negative instance as positive. It's crucial when correctly identifying all positive cases is essential.

A graphical tool used to assess the performance of binary classifiers, showing the trade-off between sensitivity (true positive rate) and specificity (1 - false positive rate).

A classifier with an ROC curve closer to the top-left corner of the graph indicates better performance. This means it can accurately identify positives while minimizing false positives.

A random classifier, lacking predictive power, would have an ROC curve close to the 45-degree diagonal, indicating no improvement over random chance.

The ROC curve is independent of the class distribution, making it valuable for evaluating classifiers that predict rare events like diseases or natural disasters.

Deploying machine learning models directly on devices like smartphones or IoT sensors, instead of relying solely on cloud servers.

Reducing the amount of data sent over the network by processing data closer to the source, on the edge device.

The time taken for a model to process data and respond is reduced because calculations happen on the device itself, close to the user.

Simplifying machine learning models by techniques like quantization or aggregation while maintaining accuracy, to make them run efficiently on resource-constrained edge devices.

A software library designed for running TensorFlow models on mobile and embedded devices, enabling efficient deployment on edge devices.

A method of deploying machine learning models where predictions are made on-demand, allowing for real-time responses.

A method of deploying machine learning models where predictions are made on a batch of data, often for offline analysis or large-scale processing.

A system that automates and schedules the execution of tasks, especially for batch processing and data pipelines.

The process of dividing a large dataset into smaller portions for parallel processing, typically in batch prediction scenarios.

Scaling data features to a common range to improve model performance and stability, especially when using gradient-based optimization algorithms.

A method to improve model performance by transferring knowledge learned from a pre-trained model on a different but related task.

A data processing framework designed to handle large volumes of data efficiently by distributing tasks across multiple machines.

Area Under the Receiver Operating Characteristic Curve (AUC) is a single value indicating a binary classifier's overall performance, ranging from 0.5 (random) to 1.0 (perfect). It considers all possible classification thresholds, making it a robust measure.

The AUC is calculated by adding consecutive trapezoid areas beneath the ROC curve, which plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at different thresholds.

The mean absolute error (MAE) is the average of the absolute differences between predicted and actual values, providing an intuitive understanding of errors.

The mean squared error (MSE) calculates the average of squared errors, giving more weight to larger errors.

The root mean squared error (RMSE) is the square root of the MSE, making it easier to interpret since it has the same units as the target variable.

Model improvement involves techniques like ensemble learning (combining multiple classifiers), cross-validation (estimating model performance), hyperparameter tuning (optimizing model parameters), and data preprocessing to enhance model accuracy and generalization.

Handling imbalanced datasets, where one class significantly outnumbers the other, is crucial for accurate classification. Techniques like oversampling, undersampling, or using algorithms specifically designed for imbalanced data can be employed.