function J = computeCost(X, y, theta) %COMPUTECOST Compute cost for linear regression % J = COMPUTECOST(X, y, theta) computes the cost of using theta as the % parameter for linear regression to fit the data points in X and y % Initialize some useful values m = length(y); % numbe...
linear regression with plottingBerry Boessenkool
b1is the slope or regression coefficient. The linear relation isy=β1x=0.0001372x. Calculate the accidents per stateyCalcfromxusing the relation. Visualize the regression by plotting the actual valuesyand the calculated valuesyCalc. yCalc1 = b1*x; scatter(x,y) holdonplot(x,yCalc1) xlabel...
function J = computeCost(X, y, theta) %COMPUTECOST Compute cost for linear regression % J = COMPUTECOST(X, y, theta) computes the cost of using theta as the % parameter for linear regression to fit the data points in X and y % Initialize some useful values m = length(y); % numbe...
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 = []; % ...
The most noticeable aspect of a regression model is the equation it produces. This model equation gives a line of best fit, which can be used to produce estimates of a response variable based on any value of the predictors (within reason). We call the output of the model a point estimate...
# Function to flatten 2D lists so it can be used by plotlydefflatten(l):return[itemforsublistinlforiteminsublist]# Set up and fit the linear regressorlin_reg=LinearRegression()lin_reg.fit(X_train,y_train)# Flatten the prediction and expected listspredicted=flatten(lin_reg.predict(X_test)...
regressionshould be your first choice. Buildingalogisticregressionmodel Plotting an ROC curve...Regressionwith scikit-learn Fit &predictforregressionTrain/test split forregression Machine learning吴恩达第三周 Logistic Regression ; 3.Predict4.RegularizedLogisticRegressionCost & RegularizedLogisticRegressionGradient...
Steps in Linear Regression y=c⋅x+m Plot the Data Points: Start by plotting the given data points, such as (1,5), (2,8), and (3,11). Adjust the Line: Draw a straight line and iteratively adjust its direction to minimise the distance (error) between the line and the data points...
(Gradient Descent in Practice II - Learning Rate)4.5 特征和多项式回归(Features and Polynomial Regression)4.6 正规方程(Normal Equation)4.7 不可逆性正规方程(Normal Equation Noninvertibility)5 Octave/Matlab Tutorial5.1 Basic Operations5.2 Moving Data Around5.3 Computing on Data5.4 Plotting Data5.x 常用...