machine learning sample code

PrefaceThe Machine learning section records Some of the notes I have learned in the process of learning, including the online course or tutorial's study notes, the reading notes of the papers, the debugging of algorithmic code, the thinking of cutting-edge theory and so on, which will open different column series for d

--Machine How to learn better (3) machine learning Cornerstone Note 16-- How the machine can learn better (4) Viii. Noise and ErrorNoise (clutter) and errors.8.1 Noise and Probabilistic TargetNoise (clutter) and probabilistic objective functions.This section focuses on whether the VC limit is still available in the cas

Anyone who knows a little bit about supervised machine learning will know that we first train the training model, then test the model effect on the test set, and finally deploy the algorithm on the unknown data set. However, our goal is to hope that the algorithm has a good classification effect on the unknown data set (that is, the lowest generalization error), why the model with the least training error w

Python machine learning decision tree and python machine Decision Tree Decision tree (DTs) is an unsupervised learning method for classification and regression. Advantages: low computing complexity, easy to understand output results, insensitive to missing median values, and the ability to process irrelevant feature da

--Machine How to learn better (3) machine learning Cornerstone Note 16-- How the machine can learn better (4) the VC DimensionVC Dimension.7.1 Definition of VC DimensionDefinition of VC dimension.Make a brief summary of the previous chapter: if there is a breakthrough in a hypothetical space. It is certain that the gro

using different methods. Take a look at the following diagram:The picture on the top left is the data set. The data is classified into the categories, red and blue. The data is hypothetical, however it could represent almost anything:coin weights and diameters, number of petals on a PL Ant and their widths, etc. Clearly there is some definite grouping here. Everything in the upper left belongs to the red category, and the bottom right to blue. However in the middle there is some crossover. If y

the clustering effect.From the machine learning point of view, the cluster is the same as the hidden pattern, the biggest difference between clustering and classification is that the classification learning instances or data objects have category tags, but clustering is different, need to be automatically determined by clustering

square feet size house to sell, then based on the above data, how do you speculate about how much the house worth. For this problem, we can apply the machine learning algorithm, draw a line in this set of data or a line of quasi-unity, according to this line we can speculate that the house may sell $000. Of course, this is not the only algorithm, such as a two-time function may be more suitable for the exi

differ in the number of samples used per learning (updating model parameters), and each update using a different sample results in different accuracy and learning time for each study. Batch gradient drop (batch gradient descent) update the model parameters with the full amount of training set samples each time, namely: Θ=θ?η?? Θj (θ) its