Linear Regression The Normal Equation Computational Complexity 线性回归模型与MSE. the normal equation: a closed-form solution to find the value of θ that minimize the cost function. generate some linear-looking data to test this equation. inv() to compute t…

前几章在不知道原理的情况下,已经学会使用了多个机器学习模型机器算法.Scikit-Learn很方便,以至于隐藏了太多的实现细节. 知其然知其所以然是必要的,这有利于快速选择合适的模型.正确的训练算法.合适的超参数.了解底层有助于更有效率地调试问题以及平台错误. 本章从现行回归模型开始,讨论两种不同的训练方式: 直接使用解析解,例如一元二次方差的求根公式. 有些数学问题(比如大多数偏微分方程)是没有数值解的,这时候就要用数值解来近似求解.有时间为了效率,解释存在解析解,也是求近似的数值解. 4.1…

if you aggregate the predictions of a group of predictors,you will often get better predictions than with the best individual predictor. a group of predictors is called an ensemble:this technique is called Ensemble Learning,and an Ensemble Learning a…

Decision Trees are versatile Machine Learning algorithms that can perform both classification and regression tasks,and even multioutput tasks. Decision Trees are the fundamental components of Random Forests,which are among the most powerful Machine L…

属性与特征: attribute: e.g., 'Mileage' feature: an attribute plus its value, e.g., 'Mileage = 15000' Note that some regression algorithm can be used for classification as well,and vice versa. For example,Logistic Regression is commonly used for classifica…

这篇论文介绍了一种创建柔性形状模型(Flexible Shape Models)的方法--点分布模型(Point Distribution Model).该方法使用一系列标记点来表示形状,重要的是根据所有训练样本计算出平均形状(Average Shape)和平均形状主要的变化模式(Modes of Variation).其中变化模式描述了形状从平均形状变化到样本形状的主要变化方式,比如长度拉伸.面积变大等.模型只有少量的线性独立的参数,这句话的意思后面会解释.      与柔性形状模型相对应的是…

the main steps: 1. look at the big picture 2. get the data 3. discover and visualize the data to gain insights 4. prepare the data for machine learning algorithms 5. select a model and train it 6. fine-tune your model 7. present your solution 8. laun…

many Machine Learning problems involve thousands or even millions of features for each training instance. not only does this make training extremely slow,it can also make it much harder to find a good solution. this problem is often referred to as th…

MNIST fetch_openml returns the unsorted MNIST dataset, whereas fetch_mldata() returned the dataset sorted by target (the training set and the test set were sorted separately). import numpy as np def sort_by_target(mnist): reorder_train = np.array(sor…

SVM is capable of performing linear or nonlinear classification,regression,and even outlier detection. SVMs are particularly well suited for classification of complex but small- or medium-sized datasets. Linear SVM Classification: Soft Margin Classif…