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This article shows you how to use the KNN nearest neighbor algorithm, the content is concise and easy to understand, can definitely brighten your eyes, through the detailed introduction of this article, I hope you can get something.

1. What is knn algorithm

Baidu encyclopedia has been introduced in detail, here is only a brief introduction.

There is a sample data set, also known as the training sample set, and there is a label for each data in the sample set, that is, we know the corresponding relationship between each data in the sample set and its classification. After inputting the new data without label, each feature of the new data is compared with the corresponding feature of the data in the sample set, and then the algorithm extracts the classification label of the most similar data (nearest neighbor) in the sample set. Generally speaking, we only select the first K most similar data in the sample data set, which is the origin of K in the K-nearest neighbor algorithm, which is usually an integer no more than 20. Finally, the classification with the most frequent occurrence of K most similar data is selected as the classification of the new data.

Advantages and disadvantages of 2.knn algorithm:

Advantages: high precision, insensitive to abnormal values, no data input assumption.

Disadvantages: high time complexity and high space complexity.

Applicable data range: numerical type and nominal type.

3. Using knn to solve classification problems

1, guide package

From sklearn.neighbors import KNeighborsClassifier

Import numpy as np

Import sklearn.datasets as datasets# this data set, there is a lot of data about data analysis.

From sklearn.model_selection import train_test_split

Import matplotlib.pyplot as plt

% matplotlib inline

two。 Import the iris data set that comes with sklearn

Iris_data = datasets.load_iris ()

3. Get the characteristics and labels of the dataset

Data = iris_data.data

Label=iris_data.target

4. View feature name

Iris_data.feature_names

It is found that the dataset has four characteristics, ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'], which correspond to each column of data in its data one by one.

5. Split training set and test set

# data is divided into two, and the training data is called "training data", "training data", "training data" and "training data".

# target is divided into two parts.

# y_train target value, y_test (retained true value)

Xerox, train_test_split, data,target,test_size=0.1, and so on.

6. Instantiate the algorithm and start training data

# declare algorithm

Knn = KNeighborsClassifier ()

# training data

Knn.fit (Xerox powder powder and yellowtrain)

7. View accuracy

Knn.score (xproof test yearly test)

8. Test and compare the predicted results with the real results

# Test and only send the data to model for prediction

Y _ = knn.predict (x_test)

Y_

Y _ is the predicted result, and y_test is the real label. Just compare it.

9. Display data

# get training samples

# process the original data to obtain the length and width of the calyx

# draw and show these flowers

X_train = data [:,: 2]

Y_train = ta

Use a three-point diagram to display data

Plt.scatter (X_train [:, 0], X_train [0RJ Magne1], cymbals, paperweights, cmap = "rainbow")

The above content is how to use the KNN nearest neighbor algorithm. Have you learned any knowledge or skills? If you want to learn more skills or enrich your knowledge reserve, you are welcome to follow the industry information channel.

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