线性回归 代码实现Linear regression importpandasaspdimportnumpyasnpimportmatplotlib.pyplotaspltdataset_scores=pd.read_csv('data/studentscores.csv')# 按Scores列降序排序数据# dataset_scores_sorted = dataset_scores.sort_values(
),不相关(图像不具有单调性) 1.3计算相关系数 ###相关系数的计算:相关系数的计算结果的绝对值越接近于1,表明这两个变量之间的相关性越高的,大于1是正相关,小于0是负相关; import pandas...# 导入sklearn.linear_model模块中的LinearRegression函数 from sk...
y, theta) computes the cost ofusingthetaasthe4% parameterforlinear regression to fit the data pointsinX and y56%Initialize some useful values7m = length(y); %number of training examples89% You need toreturnthe following variables correctly...
names = ["CRIM", "ZN", "INDUS", "CHAS", "NOX", "RM", "AGE", "DIS", "RAD", "TAX", "PTRATIO", "B", "LSTAT", "MEDV"] data = pandas.read_csv(file_path, names=names, delim_whitespace=True) # 删除包含缺失值的数据行 data = data.dropna() return data def preprocess_data(...
Stanford机器学习练习的Linear Regression部分有哪些难点? warmUpExercise.m 代码语言:javascript 代码运行次数:0 运行 AI代码解释 function A = warmUpExercise() %WARMUPEXERCISE Example function in octave % A = WARMUPEXERCISE() is an example function that returns the 5x5 identity matrix A = []; % ...
pause;%% === Part 3: Normal Equations ===fprintf('Solving with normal equations...\n');%% Load Datadata = csvread('ex1data2.txt'); X = data(:,1:2); y = data(:,3); m =length(y);% Add intercept term to XX = [ones(m,1) X];% Calculate the parameters from the normal ...
The dataset contains x and y values: x values are just iterating values. y values depend on the equation y = mx+c. Inspiration Everyone on this planet should be familiar (at least Computer Science students, etc.) about Linear Regression, so calculate the trend line, R^2, coefficient and...
from sklearn.linear_modelimportLinearRegression from sklearn.metricsimportmean_squared_error # read the train and test dataset train_data=pd.read_csv('train.csv')test_data=pd.read_csv('test.csv')print(train_data.head())# shapeofthe datasetprint('\nShape of training data :',train_data.sh...
from sklearn.linear_model import LinearRegression # 读取数据集 dataset = pd.read_csv('data.csv') # 准备自变量和因变量 X = dataset.iloc[:, :-1].values y = dataset.iloc[:, -1].values # 创建模型对象并训练 model = LinearRegression() model.fit(X, y) # 使用模型进行预测 y_pred = mo...
TensorFlow是咱们机器学习领域非常常用的一个组件,它在数据处理,模型建立,模型验证等等关于机器学习方面的领域都有很好的表现,前面的一节我已经简单介绍了一下TensorFlow里面基础的数据结构即:Tensor和Dataset; 这里咱们开始介绍TensorFlow的建模过程以及验证模型的一些简单方法。其实无论是sklearn还是TensorFlow,他们的模型建立...