qq_31723281的博客

一、机器学习步骤 一般的步骤为： 1）数据采集与标记 2）特征选择 3）数据清洗 4）模型选择 5）模型训练 6）模型性能评估与优化 二、sklearn--svm 1、数据采集 from ...

import numpy as np from sklearn.pipeline import Pipeline from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LinearRegression from sklearn.model_selection import lea...

一、算法原理 kNN的核心思想是未标记样本的类别，由距离其最近的k个邻居投票决定。计算待标记的数据样本和数据集中每个样本的距离，取距离最近的k个样本。 假设X_test为待标记的数据样本,X_train为已标记的数据集，伪代码如下： 1）遍历X_train 中的所有样本，计算每个样本与X_test的距离，并且把距离保存在distance数组中 ...

from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression import time boston = load_boston() X = boston.data y ...

from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression cancer = load_breast_cancer() X = cancer.data...

from sklearn import svm import numpy as np import time from sklearn.model_selection import ShuffleSplit from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_...

from time import time from sklearn.naive_bayes import MultinomialNB from sklearn.datasets import load_files from sklearn.feature_extraction.text import TfidfVectorizer print("loading train dataset.....

"""用pca降维后一个人脸识别的实列""" from sklearn.datasets import fetch_olivetti_faces import time import numpy as np import logging from matplotlib import pyplot as plt from sklearn.model_selection import train_...

from sklearn.datasets import make_blobs from matplotlib import pyplot as plt from sklearn.cluster import KMeans X, y = make_blobs(n_samples=200, n_features=2, cen...