"regression score" is used to evaluate how well a KNN model performs on regression tasks (predicting continuous values)
In regression tasks, the score is often determined using metrics like mean squared error (MSE) or mean absolute error (MAE). These metrics quantify how well the KNN model's predictions match the actual target values.
Example of KNN Regression Score:
Consider a regression problem where you're trying to predict the price of used cars based on their age and mileage. After training your KNN regression model, you use it to make predictions on a set of test cars and calculate the MSE.
Suppose the MSE is 2000, which means, on average, the squared difference between the predicted prices and actual prices for the test cars is 2000. A lower MSE indicates a better fit, so the lower the MSE, the better your KNN regression model is performing.
In the context of KNN, a "regression score" typically refers to a metric used to assess the performance of a KNN model for regression tasks.
- Regression tasks involve predicting a continuous target variable (e.g., predicting the price of a house, the temperature, or a numerical value).
- Common regression score metrics include Mean Squared Error (MSE), Mean Absolute Error (MAE), R-squared (R²), etc.
- The goal of a regression score is to measure how well the KNN model's predictions align with the actual target values . Example of KNN Regression Score:
Suppose you have a dataset with the age and annual income of individuals and want to predict their annual expenses. After training a KNN regression model, you evaluate its performance. If the model's MSE is 5000, it means, on average, the squared difference between its predictions and the actual expenses is 5000. A lower MSE indicates a better fit, meaning the KNN regression model's predictions are closer to the actual values.
"KNN score" in the context of classification assesses how well the KNN model performs on classification tasks (categorizing data into classes).
KNN Score in Classification:
- In the context of KNN classification, the "KNN score" typically refers to how well a KNN model performs on classification tasks.
- Classification tasks involve predicting categorical labels or classes (e.g., classifying emails as spam or not spam, identifying flower species, etc.).
- Common classification score metrics include accuracy, precision, recall, F1-score, ROC-AUC, etc.
- The KNN score in classification measures the model's ability to correctly classify instances into their respective classes . Example of KNN Classification Score:
Suppose you have a dataset of emails labeled as spam and not spam. You've trained a KNN classifier to predict whether an email is spam. After testing it on a set of emails, you calculate the accuracy. If the KNN classifier achieves an accuracy of 95%, it correctly classifies 95% of the emails in the test set.
In classification tasks, the score often refers to the accuracy, which is a commonly used metric to evaluate the performance of a KNN classifier. Accuracy measures the proportion of correctly classified instances out of the total instances in the dataset.
Example of KNN Classification Score:
Let's say you have a dataset of flowers with two features, sepal length and sepal width, and three classes: "Setosa," "Versicolor," and "Virginica." You've trained a KNN classifier to predict the species of the flowers based on these features.
Suppose you have 100 test samples, and your KNN classifier correctly classifies 85 of them. In this case, the accuracy score of your KNN model is 85/100, or 85%