max(x), 100) y_fit = intercept + slope * x_fit # 绘制原始数据点和拟合曲线 plt.scatter(x, y, label='Data points') plt.plot(x_fit, y_fit, color='red', label='Fitted line') plt.xlabel('x') plt.ylabel('y') plt.title('Linear Regression using Least Squares') plt.legend() ...
OLS Regression Results === Dep. Variable: y R-squared: 0.535 Model: OLS Adj. R-squared: 0.461 Method: Least Squares F-statistic: 7.281 Date: Tue, 19 Feb 2013 Prob (F-statistic): 0.00191 Time: 21:51:28 Log-Likelihood: -26.025 No. Observations: 23 AIC: 60.05 Df Residuals: 19 BIC: ...
Linear models Ridge regression Logistic regression Ordinary least squares Gaussian naive Bayes classifier Generalized linear model (identity, log, and logit links) Bayesian linear regression w/ conjugate priors Unknown mean, known variance (Gaussian prior) Unknown mean, unknown variance (Normal-Gamma / N...
The factors.py module includes common approximate matrix-factorization algorithms including: Regularized alternating least squares (ALS) Non-negative matrix factorization via fast hierarchical least squares (HALS) (Cichocki & Phan, 2008) numpy-ml\numpy_ml\factorization\__init__.py 代码语言:javascript 复...
Gaussian process regression Matrix factorization Regularized alternating least-squares Non-negative matrix factorization Preprocessing Discrete Fourier transform (1D signals) Discrete cosine transform (type-II) (1D signals) Bilinear interpolation (2D signals) ...
线性回归(linear regression)是一种线性模型,它假设输入变量 x 和单个输出变量 y 之间存在线性关系 用户9925864 2022/07/27 5710 一元回归分析 编程算法 谈论的是预测区间,两者是不同的,显然,预测区间要比置信区间宽很多. 要提高预测区间(置信区间也一样) 的精度,即要使 用户3577892 2021/01/14 1.1K0 机器学...
OLS Regression Results === Dep. Variable: y R-squared (uncentered): 0.430 Model: OLS Adj. R-squared (uncentered): 0.413 Method: Least Squares F-statistic: 24.42 Date: Sun, 14 Jun 2020 Prob (F-statistic): 7.44e-12 Time: 10:04:35 Log-Likelihood...
Gaussian process regression Matrix factorization Regularized alternating least-squares Non-negative matrix factorization Preprocessing Discrete Fourier transform (1D signals) Discrete cosine transform (type-II) (1D signals) Bilinear interpolation (2D signals) ...
Linear regression algorithms: There are many ways to find the coefficients and the intercept, you can use least squares or one of the optimisation methods like gradient decent In this post we will use least squares: Least Squares Least Squares is method a find the best fit line to data. It...
Gaussian process regression Matrix factorization Regularized alternating least-squares Non-negative matrix factorization Preprocessing Discrete Fourier transform (1D signals) Discrete cosine transform (type-II) (1D signals) Bilinear interpolation (2D signals) Nearest neighbor interpolation (1D and 2D signals...