- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost

- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece

- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting

A

C

D

G

L

M

N

P

R

S

T

X

- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost

- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece

- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting

Precision and Recall are two evaluation metrics used to determine the effectiveness of a classification model. These metrics help to measure the quality of the model by looking at the number of true positives, true negatives, false positives, and false negatives.

Precision is the ability of a classification model to identify only the relevant data points. It is the fraction of the true positives predicted by the model out of all the true and false positives. For example, if a spam filter marks an email as spam and it is indeed spam, then this is counted as a true positive. Precision is calculated as:

**Precision = True Positives / (True Positives + False Positives)**

Recall, on the other hand, is the ability of a classification model to find all the relevant data points. It is the fraction of the true positives predicted by the model out of all the true positives and false negatives. For example, if a spam filter misses a spam email, then this is counted as a false negative. Recall is calculated as:

**Recall = True Positives / (True Positives + False Negatives)**

When a classification model is trained, it makes predictions based on the features of an input. These predictions can be either positive or negative, and the goal is to identify as many true positives and true negatives as possible. However, the model may also produce a certain number of false positives (i.e., incorrectly identifying a negative as a positive) and false negatives (i.e., incorrectly identifying a positive as a negative).

Precision and Recall measure the effectiveness of a classification model in identifying true positives and true negatives while minimizing false positives and false negatives.

Precision and Recall are important because they measure the accuracy of a classification model. High precision means that the model is accurately identifying true positives and true negatives with minimal false positives. High recall means that the model is identifying most of the true positives and true negatives in the dataset.

Businesses use precision and recall to evaluate the performance of their machine learning models and to optimize their models for specific applications. By using precision and recall, businesses can ensure that their models are making accurate predictions and minimizing errors.

Precision and recall are important metrics for evaluating the effectiveness of machine learning models in classification tasks. By understanding these metrics, businesses can improve their data engineering and analytics capabilities and gain a competitive edge. H2O offers a robust suite of tools and libraries for machine learning that make it easy for data scientists to implement precision and recall in their projects.