即y=2x1+51x2+21.2 目标是通过数据训练使得w和b靠近w =[2,51],b = 21.2,换句话说就是通过训练得到一个平面能够跟实际的平面(y=2x1+51x2+21.2)一致。 -代码实现- 回顾深度学习的套路: 准备数据集dataset 构建网络(激活函数activation function) 初始化 训练(epochs,更新权重) 预测 所用的深度学习框架为...
data=load_exdata('ex1data2.txt');data=np.array(data,np.int64)x=data[:,(0,1)].reshape((-1,2))y=data[:,2].reshape((-1,1))m=y.shape[0]# Print out some data pointsprint('First 10 examples from the dataset: \n')print(' x = ',x[range(10),:],'\ny=',y[range(10),:...
# import regression as lr lr.ridgeTestPlot() # import regression as lr lr.stageWiseTestPlot() from numpy import * # txt 文件数据提取 以TAB键值分割 def loadDataSet(fileName): numFeat = len(open(fileName).readline().split('\t')) - 1 #样本数据维度 最后一个为标签 dataMat = []; labe...
x_train = x.reshape(-1,1).astype('float32') y_train = y.reshape(-1,1).astype('float32')classLinearRegressionModel(nn.Module):def__init__(self, input_dim, output_dim):super(LinearRegressionModel,self).__init__()self.linear = nn.Linear(input_dim, output_dim)defforward(self, x)...
python machine-learning linear-regression-models xgboost-algorithm Updated Feb 15, 2025 Jupyter Notebook s1dewalker / Model_Validation Star 0 Code Issues Pull requests Model Management in Python. Steps involved in Model Validation and tuning. Testing Model Assumptions in Factor Analysis with OLS...
pythonchallengedata-sciencemachine-learningcorrelationanalyticsrandom-forestlinear-regressiondata-engineeringdatasetpolynomial-regressionlinear-regression-modelspt-brrandom-forest-classifiercall-centerkeyruscall-center-analytics UpdatedJan 11, 2022 Python SavanK/FakeNewsChallenge ...
来看使用python的scikit-learn完成的线性回归案例: 上文代码块 代码内容: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # importing required librariesimportpandasaspd from sklearn.linear_modelimportLinearRegression from sklearn.metricsimportmean_squared_error ...
目录1What isregression? 2 What is aregressionmodel?3Types ofregressionmodels 4 Applications ofregression5Regressionalgorithms Coursera 课程Machinelearningwith python1What isregression 智能推荐 Shallow Learning—Linear Regression Regression(回归):通过训练输出一个数值。(训练是指寻找合适的function的过程) eg: ...
Gradient descent is often taught using a linear regression model because it is relatively straightforward to understand. In practice, it is useful when you have a very large dataset either in the number of rows or the number of columns that may not fit into memory. 4. Regularization There are...
Figure 1: Simulated data for linear regression problem In this example, the data samples represent the feature and the corresponding targets . Given this dataset, how can we predict target as a function of ? This is a typical regression problem. ...