machine learning algorithms cheat sheet pdf

Continuous update ...1.k-Nearest Neighbor algorithmAdvantages: High precision, insensitive to outliers, no data input settingsCons: High computational complexity, high spatial complexityApplicable data range: Numerical and nominal typeApplicable scenarios:2.ID3 Decision Tree AlgorithmAdvantages: The computational complexity is not high, the output is easy to understand, the missing middle value is not sensitive, can process the irrelevant characteristic dataDisadvantage: May cause over-matching

Supervised learningKNN k Nearest Neighbor algorithmdef classify0 (InX, DataSet, labels, k):Datasetsize = dataset.shape[0]Diffmat = Tile (InX, (datasetsize,1))-DataSetSqdiffmat = diffmat**2Sqdistances = Sqdiffmat.sum (Axis=1)distances = sqdistances**0.5classcount={}For I in range (k):Voteilabel = Labels[sorteddistindicies[i]]Classcount[voteilabel] = Classcount.get (voteilabel,0) + 1Sortedclasscount = sorted (Classcount.iteritems (), Key=operator.itemgetter (1), reverse=true)return sortedclasscoun

training samples.The above two or three can be done in the case of inverse existence, but what if the characteristics of the data are more than the sample points, because the inverse is not present at this time? You can use the ridge regression method to solve this problem, that is, it will be converted to, the other and the previous approach is similar.Of course, there is a method called forward stepwise regression, it is through each step to a certain weight increase or decrease a small value

paper is usually European-style distance, Pearson coefficient or cosine similarity.Assuming that a matrix A is established, the M*n matrix, the rows are all users, n is all items, each element of the matrix represents the user's rating of the item, then the item-based or user-based recommendation is to calculate the similarity of all columns or all rows. In real life, this matrix is very sparse.Topic: Recommend users to buy TOPN itemsThe Matrix C is a m*n matrix, each row represents each user,

One of the top ten algorithms for Machine Learning: EM algorithm. One of the top 10, which makes people think Nb-rich. What is Nb? We generally say someone is Nb because he can solve problems that others cannot solve. Why God is God, because God can do things that many people cannot do. So what problems can the EM algorithm solve? Or the reason why the EM algorit

The idea of clustering: dividing a DataSet into several subsets (called a cluster cluster) that you don't want to cross, each potentially corresponding to a concept. But the practical significance of each cluster is determined by the users themselves, and the clustering algorithm will only be divided.The role of Clustering:1) can be used as a separate process for finding a distribution pattern of data2) as a preprocessing process for classification. First, classify data is clustered and then the

Naive Bayesian algorithm is to look for a great posteriori hypothesis (MAP), which is the maximum posteriori probability of the candidate hypothesis.As follows:In Naive Bayes classifiers, it is assumed that the sample features are independent from one another:Calculate the posterior probability of each hypothesis and choose the maximum probability, and the corresponding category is the result of the sample classification.Advantages and DisadvantagesVery good for small-scale data, suitable for mu

In machine learning, there are many problems, there is no analytic form of solution, or analytic form of the solution but the computation is very large (for example, the problem of the least-squares solution), for such problems, we usually choose to use an iterative optimization method to solve.These commonly used optimization algorithms include gradient descent

can be processed.Cons: Easy to fit.How to avoid overfitting:(1) dimensionality reduction, can use PCA algorithm to reduce the dimension of the sample, so that the number of theta of the model is reduced, the number of times will be reduced, to avoid overfitting;(2) regularization, the design of regular items regularization term.The regularization function is to prevent some properties before the coefficient weight is too large, there has been a fitting.Note that the way to resolve overfitting i