kaggle machine learning datasets

Algorithm. These algorithms are greedy algorithms, top-down, just choose the attributes of the measurement method is Different. 4, tree pruning leaves (avoid overfitting) when the depth of the tree is too large, the design algorithm in the training set performance will be better, but the performance on the test set will be very general, then we will be a certain crop of trees: (1) first pruning When you get to a certain level, you don't grow trees down. (2) after pruning The t

Machine learning must be familiar to support vector machines (support vector MACHINE-SVM), because before the depth of learning, SVM has been hogging the machine to learn Big Brother's seat. His theory is very beautiful, various varieties of improved version also many, such

For a machine learning system, there are several problems to be solved: 1, how to choose Feature. 2, which algorithm to choose. 3, how to set the parameters for this algorithm. Together, these questions are "how to choose a model". For example: can realize the classification system algorithm has one-vs-all logistic regression,neural NETWORK,SVM and so on, we should use which one. To solve this problem, we

The Generative Warfare network (GAN, generative adversarial Networks) is a deep learning model, which is one of the most promising methods for unsupervised learning in the complex distribution. The model produces quite good output through the mutual game Learning of the framework (at least) two modules: the generation model (generative models) and discriminant mo

Pattern Recognition field Application machine learning scene is very many, handwriting recognition is one of the most simple digital recognition is a multi-class classification problem, we take this multi-class classification problem to introduce Google's latest open source TensorFlow framework, The content behind the deep learning will be presented and demonstra

Full Stack Engineer Development Manual (author: Shangpeng) Python Tutorial Full solution installation Pip Install LIGHTGBM Gitup Web site: Https://github.com/Microsoft/LightGBM Chinese Course http://lightgbm.apachecn.org/cn/latest/index.html LIGHTGBM Introduction The emergence of xgboost, let data migrant workers farewell to the traditional machine learning algorithms: RF, GBM, SVM, LASSO ... Now Microsoft

Recently, when I was studying pattern recognition, neural networks, and other courses, I found that I could not find a dataset to train the classifier or network I constructed. I tried to find such a dataset some time ago, I have not found any of the major forums. I finally found a database in a paper, which is a database (UCI) of the famous University of California, Leon) Http://www.ics.uci.edu /~ Mlearn/mlrepository.html In addition, I did some support vector

machine learning algorithms.In this section, we mainly introduce the use of naive Bayesian method for the classification of text, we will use a set of tagged categories of text documents to train naive Bayesian classifier, and then to the unknown data instances of the category prediction. This method can be used as a filter for spam messages.Data setThe data of this experiment can get a set of news informa

Datasets: Exposing datasets100+ interesting data sets for statistical data http://rs.io/100-interesting-data-sets-for-statistics/Data Set subreddit https://www.reddit.com/r/datasetsUCI Machine Learning Library http://archive.ics.uci.edu/ml/ information : From a personal bloghttp://www.cnblogs.com/hellochennan/p/5352110.htmlhttp://www.cnblogs.com/hellochenn

Using Python3 to learn the API of linear regressionPrediction of benign and malignant tumors using logistic regression and stochastic parameter estimation regression respectivelyI downloaded the dataset locally and can come to my git to download the source code and dataset:Https://github.com/linyi0604/kaggle1 ImportNumPy as NP2 ImportPandas as PD3 fromSklearn.cross_validationImportTrain_test_split4 fromSklearn.preprocessingImportStandardscaler5 fromSklearn.linear_modelImportlogisticregression

[TOC]This article refers to the Spark rapid Big data analysis, which summarizes the use of the RDD and mllib of the spark technology core and several of its key libraries. Initialize Operation Spark Shell:bin/pysparkEach spark application consists of a drive program (driver programs) that initiates various parallel operations on the cluster, the drive program contains the main function of the application, and the distributed datasets on the cluster ar

Data Source: Http://archive.ics.uci.edu/ml/datasets/WineReference: "Machine learning Python Combat" Wei originalPurpose of the blog: reviewTool: Geany#导入类库From pandas import Read_csv #读数据From pandas.plotting import Scatter_matrix #画散点图From pandas import set_option #设置打印数据精确度Import NumPy as NPImport Matplotlib.pyplot as Plt #画图From sklearn.preprocessing import nor

" ) plt.show () 7.3.6 Ridge Regression Implementation and K-value determination#The first 8 columns are arr, and the post 1 column is YarrXarr,yarr = Loaddataset ('Abalone.txt') Xmat,ymat= Normdata (Xarr,yarr)#Standardize data setsKnum= 30#determine the number of iterations of KWmat = Zeros ((Knum,shape (Xmat) [1])) Klist= Zeros ((knum,1)) forIinchxrange (knum): K= Float (i)/500#The purpose of the algorithm is to determine the value of KKlist[i] = k#List of k valuesXTx = xmat.t*Xmat denom= x

Basic conceptual data for machine learning Famous Iris Iris Data Https://en.wikipedia.org/wiki/lris_flower_data_set Lris Setossa lris versicolor lris VerginicaHere is the data for Iris: Data collectively called Datasets (data Set) Each row of data is called a sample (sample) In addition to the last column, each column expresses a ch

2018.4.18Python machine learning record one. Ubuntu14.04 installation numpy1. Reference URL 2. Installation code: It is recommended to update the software source before installing: sudo apt-get update If Python 2.7 is not a problem, you can proceed to the next step.The packages for numeric calculations and drawings are now installed and Sklearn are numpy scipy matplotlib Pandas and Sk

value of 3.For example: Np.random.randint (3, 6, size=[2,3]) returns data with a dimension of 2x3. The value range is [3,6].(4). Random_integers (low[, high, size]), similar to the above randint, the difference between the range of values is closed interval [low, high].(5). Random_sample ([size]), returns the random floating-point number in the half-open interval [0.0, 1.0]. If it is another interval [a, b), it can be converted (b-a) * Random_sample ([size]) + AFor example: (5-2) *np.random.ran

In this paper, "machine learning Combat" study notes 1. Introduction to Decision Tree A decision tree can extract a series of rules from a collection of data, and the process of creating a rule is the process of machine learning. In the process of constructing the decision tree, the feature partition dataset is selecte

generate a Boolean VectorTIPS: here is why we need to multiply a vector of all 1, because we need to convert the boolean type to the int type. You can try it in Python, >>> (1 = 1) * 11 4. When the overall error of all weak classifiers is 0, no further iteration is required. Next let's take a look at how to use the Adaboost algorithm for classification. def adaClassify(datToClass,classifierArr): dataMatrix = mat(datToClass) m = shape(dataMatrix)[0] aggClassEst = mat(zeros((m,1)))

Http://archive.ics.uci.edu/ml/ The database is a machine learning database proposed by the University of California at the University of Virginia (universityofcaliforniairvine). There are currently 187 datasets in this database, and the number of these databases is increasing, UCI dataset is a common standard test dataset. The "multiplefeatures" database on UCI

= {bestfeatlabel:{}} #创建节点del (labels[ Bestfeat]) featvalues = [Example[bestfeat] For example in dataset]uniquevals = Set (featvalues) for value in Uniquevals: #划分, Create subtree sublabels = Labels[:]mytree[bestfeatlabel][value] = Createtree (Splitdataset (dataset,bestfeat,value), subLabels) Return mytreeThus, the decision tree is constructed.Construction Result:Using Decision Trees:def classify (Inputtree,featlabels,testvec): Firststr = Inputtree.keys () [0]seconddict = Inputtree[firststr]fea