- Machine Learning Basics
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- Machine Learning Data Visualization
- Machine Learning - Data Visualization
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- Statistics for Machine Learning
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- Regression Analysis In ML
- Machine Learning - Regression Analysis
- Machine Learning - Linear Regression
- Machine Learning - Simple Linear Regression
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- Classification Algorithms In ML
- Machine Learning - Classification Algorithms
- Machine Learning - Logistic Regression
- Machine Learning - K-Nearest Neighbors (KNN)
- Machine Learning - Naïve Bayes Algorithm
- Machine Learning - Decision Tree Algorithm
- Machine Learning - Support Vector Machine
- Machine Learning - Random Forest
- Machine Learning - Confusion Matrix
- Machine Learning - Stochastic Gradient Descent
- Clustering Algorithms In ML
- Machine Learning - Clustering Algorithms
- Machine Learning - Centroid-Based Clustering
- Machine Learning - K-Means Clustering
- Machine Learning - K-Medoids Clustering
- Machine Learning - Mean-Shift Clustering
- Machine Learning - Hierarchical Clustering
- Machine Learning - Density-Based Clustering
- Machine Learning - DBSCAN Clustering
- Machine Learning - OPTICS Clustering
- Machine Learning - HDBSCAN Clustering
- Machine Learning - BIRCH Clustering
- Machine Learning - Affinity Propagation
- Machine Learning - Distribution-Based Clustering
- Machine Learning - Agglomerative Clustering
- Dimensionality Reduction In ML
- Machine Learning - Dimensionality Reduction
- Machine Learning - Feature Selection
- Machine Learning - Feature Extraction
- Machine Learning - Backward Elimination
- Machine Learning - Forward Feature Construction
- Machine Learning - High Correlation Filter
- Machine Learning - Low Variance Filter
- Machine Learning - Missing Values Ratio
- Machine Learning - Principal Component Analysis
- Machine Learning Miscellaneous
- Machine Learning - Performance Metrics
- Machine Learning - Automatic Workflows
- Machine Learning - Boost Model Performance
- Machine Learning - Gradient Boosting
- Machine Learning - Bootstrap Aggregation (Bagging)
- Machine Learning - Cross Validation
- Machine Learning - AUC-ROC Curve
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- Machine Learning - Train and Test
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- Machine Learning - Gaussian Discriminant Analysis
- Machine Learning - Cost Function
- Machine Learning - Bayes Theorem
- Machine Learning - Precision and Recall
- Machine Learning - Adversarial
- Machine Learning - Stacking
- Machine Learning - Epoch
- Machine Learning - Perceptron
- Machine Learning - Regularization
- Machine Learning - Overfitting
- Machine Learning - P-value
- Machine Learning - Entropy
- Machine Learning - MLOps
- Machine Learning - Data Leakage
- Machine Learning - Resources
- Machine Learning - Quick Guide
- Machine Learning - Useful Resources
- Machine Learning - Discussion
Machine Learning - Performance Metrics
Performance metrics in machine learning are used to evaluate the performance of a machine learning model. These metrics provide quantitative measures to assess how well a model is performing and to compare the performance of different models. Performance metrics are important because they help us understand how well our model is performing and whether it is meeting our requirements. In this way, we can make informed decisions about whether to use a particular model or not.
There are many performance metrics that can be used in machine learning, depending on the type of problem being solved and the specific requirements of the problem. Some common performance metrics include −
Accuracy − Accuracy is one of the most basic performance metrics and measures the proportion of correctly classified instances in the dataset. It is calculated as the number of correctly classified instances divided by the total number of instances in the dataset.
Precision − Precision measures the proportion of true positive instances out of all predicted positive instances. It is calculated as the number of true positive instances divided by the sum of true positive and false positive instances.
Recall − Recall measures the proportion of true positive instances out of all actual positive instances. It is calculated as the number of true positive instances divided by the sum of true positive and false negative instances.
F1 Score − F1 score is the harmonic mean of precision and recall. It is a balanced measure that takes into account both precision and recall. It is calculated as 2 * (precision × recall) / (precision + recall).
ROC AUC Score − ROC AUC (Receiver Operating Characteristic Area Under the Curve) score is a measure of the ability of a classifier to distinguish between positive and negative instances. It is calculated by plotting the true positive rate against the false positive rate at different classification thresholds and calculating the area under the curve.
Confusion Matrix − A confusion matrix is a table that is used to evaluate the performance of a classification model. It shows the number of true positives, true negatives, false positives, and false negatives for each class in the dataset.
Example
Here is an example code snippet to calculate the accuracy, precision, recall, and F1 score for a binary classification problem −
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
# Load the iris dataset
iris = load_iris()
X = iris.data
y = iris.target
# Split the dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train a logistic regression model on the training set
model = LogisticRegression()
model.fit(X_train, y_train)
# Make predictions on the test set
y_pred = model.predict(X_test)
# Compute performance metrics
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='macro')
recall = recall_score(y_test, y_pred, average='macro')
f1 = f1_score(y_test, y_pred, average='macro')
# Print the performance metrics
print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1 Score:", f1)
Output
When you execute this code, it will produce the following output −
Accuracy: 1.0 Precision: 1.0 Recall: 1.0 F1 Score: 1.0