machine learning finance book

Near the study of "machine learning Combat" This book, made some notes, and everyone to share the following:An overview of the K-Nearest neighbor algorithm (KNN)The simplest initial-level classifier is a record of all the classes corresponding to the training data, which can be categorized when the properties of the test object and the properties of a training ob

most machine learning algorithms. Normalization is usually done by taking the maximum and minimum values corresponding to each feature dimension, and then using the current eigenvalues to compare them to a number that is normalized to [0,1]. If the characteristic value is noisy, the noise should be removed beforehand."Function:auto-normalizing the feature matrix the formula Is:newvalue = (oldvalue-min)/

64 documents, the word "the" appears 1000 times in 63 documents, and Messi appears 5 times in 3 documents. The bottom is 2.The:log (64/1+63) = 0Messi:log (64/1+3) = 4Then TF * IDFthe:1000 * 0 = 0Messi:4 * 5 = 20We need a function:Defines a distance that is used to measure similarity.1. We can calculate the similarity of this article and other articles, and return an optimal result.2. We can calculate the similarity between this article and other articles, and return the K most relevant results

See original book 2.1-2.2 sectionThe new dataset is like a wrapped gift, filled with promise and hope!But until you open it, it remains mysterious!I. Structure and terminology of the underlying problem, characteristics of the machine learning data setTypically, rows represent instances, columns represent attribute characteristicsproperty, the data used in the ins

Radicals theSorteddistindicies = Distances.argsort ()#sort in ascending order, return the original subscript -ClassCount = {} - forIinchRange (k): -Voteilabel =Labels[sorteddistindicies[i]] +Classcount[voteilabel] = classcount.get (Voteilabel, 0) + 1#get是字典中的方法, preceded by the value to be obtained, followed by the default value if the value does not exist -Sortedclasscount = sorted (Classcount.items (), Key=operator.itemgetter (1), reverse=True)　　　　#在python3中没有iteritems, Key here is sorte