A detailed introduction to the kNN classifier method
The k-Nearest Neighbors (kNN) classifier is a commonly used machine learning algorithm used to classify data. The principle of the kNN classifier is based on the distance measurement between samples. It determines the category of the sample to be classified by finding the k training samples closest to the sample to be classified and voting based on the labels of these samples.
The specific steps of the kNN classifier are as follows:
1. Data preparation: Collect and prepare the data set for training, ensuring that the data set contains labeled sample points.
2. Feature selection: Select appropriate features according to the characteristics of the problem, and preprocess the features (such as normalization, standardization, etc.).
3. Calculate distance: Use an appropriate distance measurement method (such as Euclidean distance, Manhattan distance, etc.) to calculate the distance between the sample to be classified and each sample in the training set.
4. Select the nearest neighbor: Based on the distance measurement result, select the k training samples closest to the sample to be classified.
5. Category voting: Based on the labels of the nearest neighbor samples, vote to determine the category of the sample to be classified.
6. Model evaluation: Use the test data set to evaluate the performance of the model, usually using indicators such as accuracy, precision, and recall.
The advantages of the kNN classifier include:
- Simple and intuitive, easy to understand and implement.
- It has good adaptability to non-linear relationships and complex decision boundaries.
- Performs well for problems with small training data and large number of features.
However, the kNN classifier also has some limitations:
- For high-dimensional data and large-scale data sets, the complexity of calculating distance is high.
- For imbalanced data sets, there may be a bias towards the majority class.
- Appropriate k values and distance measures need to be chosen to achieve optimal performance.
Therefore, in practical applications, it is necessary to select appropriate k values and distance measurement methods based on specific problems and data characteristics, and perform model tuning to improve performance.
k |
integer, default: 1 | The number of neighbors used for classification. |
searchMethod |
String, default: "AUTO" | Search method. The following are valid [AUTO, LINEAR_SEARCH, KD_TREE, COVER_TREE]. AUTO will choose between KD_TREE and COVER_TREE based on dimension count. The results of different search methods for distance relationships and probability values may vary. As performance and results may vary, please refer to SMILE's documentation and other literature. |
metric |
String, default: "EUCLIDEAN" | The distance metric to use. NOTE: KD_TREE (and AUTO for lower dimensions) will not use the selected metric. Options are: 'EUCLIDEAN' - Euclidian |