Machine learning_K nearest neighbor algorithm, hyperparameter adjustment, naive Bayes algorithm, model evaluation

1. K nearest neighbor algorithm
(1) code example

import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# 读取数据，预测签到地点
data = pd.read_csv(r"C:\Users\dell\Desktop\机器学习\机器学习代码和资料\数据\train.csv")

# 因为是测试，为了减少运算，只选取一部分数据
data = data.query("x > 2.0 &  x < 2.25 & y > 3.5 & y < 3.75")

# 处理数据
# 1 处理时间数据，将把时间戳变成日期格式
time_value = pd.to_datetime(data['time'], unit='s')
# 2 利用上面日期格式转化为字典格式
time_value = pd.DatetimeIndex(time_value)
# 3 利用字典格式给data加上几个日期特征
data['day'] = time_value.day
data['hour'] = time_value.hour
data['weekday'] = time_value.weekday
# 4 删除时间戳
data = data.drop(['time'], axis=1)
# 5 将签到数量小于n的目标删除（place_id >= n）
place_count = data.groupby('place_id').count()
tf = place_count[place_count.row_id > 3].reset_index()
data = data[data['place_id'].isin(tf.place_id)]
# 6 删除无意义的特征值row_id
data = data.drop(['row_id'], axis=1)

# 取出数据中的特征值x和目标值y
y = data['place_id']
x = data.drop(['place_id'], axis=1)

# 分割数据，分为测试机和训练集
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)

# 特征工程   y是目标值，自然不用标准化
std = StandardScaler()
x_train = std.fit_transform(x_train)
x_test = std.transform(x_test)

# 进入算法
knn = KNeighborsClassifier()
knn.fit(x_train, y_train)

# 得出结果
y_predict = knn.predict(x_test)

# 评价预测结果
score = knn.score(x_test, y_test)
print(score)

(2) Advantages and disadvantages of K-nearest neighbor algorithm

(3) Hyperparameter adjustment and cross-validation

# 首先要去掉上面knn后面跟的参数选择,然后自己调参
 param = {
    
    "n_neighbors": [3, 5, 10]}

# 进行网格搜索，设置超参数和交叉验证的n（cv=n）
gc = GridSearchCV(knn, param_grid=param, cv=4)

gc.fit(x_train, y_train)

# 预测准确率
print("在测试集上准确率：", gc.score(x_test, y_test))

print("在交叉验证当中最好的结果：", gc.best_score_)

print("选择最好的模型是：", gc.best_estimator_)

print("每个超参数每次交叉验证的结果：", gc.cv_results_)

Second, the naive Bayes algorithm

(1) Principle (Bayesian formula)

(2) Code example

from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.datasets import fetch_20newsgroups

# 调用import来的数据
news = fetch_20newsgroups(subset='all')

# 进行数据分割
x_train, x_test, y_train, y_test = train_test_split(news.data, news.target, test_size=0.25)

# 因为是对文本分类，所以要对数据集进行特征抽取
tf = TfidfVectorizer()

# 以训练集当中的词的列表进行每篇文章重要性统计
x_train = tf.fit_transform(x_train)
x_test = tf.transform(x_test)

# 进行朴素贝叶斯算法的预测
mlt = MultinomialNB(alpha=1.0)
mlt.fit(x_train, y_train)
y_predict = mlt.predict(x_test)

# 得出准确率
print("准确率为：", mlt.score(x_test, y_test))
print("每个类别的精确率和召回率：", classification_report(y_test, y_predict, target_names=news.target_names))

3. Evaluation of the model
(1) Confusion matrix

Accuracy: the proportion of the sample that is predicted to be positive

Recall rate: the proportion of samples whose real positive examples are predicted to be positive

F1score: Comprehensive evaluation index

Calculation code:

sklearn.metrics.classification_report(y_true, y_pred, target_names=None)

y_true：真实目标值
y_pred：估计器预测目标值
target_names：目标类别名称
return：每个类别精确率与召回率

(2) Cross validation

Cross validation: Divide the obtained data into training and validation sets. Take the following figure as an example: divide the data into n parts, one of which is used as the verification set. Then after n times (groups) of tests, a different verification set is changed each time. That is, the results of n groups of models are obtained, and the average value is taken as the final result. Also known as n-fold cross-validation.

Example: The picture below shows five-fold cross-validation