kaggle machine learning datasets

Scikit-learn this very powerful Python machine learning ToolkitHttp://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.htmlS1. Import dataMost of the data is formatted as M n-dimensional vectors, divided into training sets and test sets. So, knowing how to import vector (matrix) data is the most critical point. We need to use NumPy to help. Suppose the data format is: Stock Prices I

ProfileThe commonly used machine learning algorithms:\ (k\)-Nearest neighbor algorithm, decision tree, naive Bayesian,\ (k\)-mean clustering its ideas and Python code implementation summary. Do not have to know it but also know the reason why. Refer to "machine learning combat".? ?\ (k\)-Nearest Neighbor algorith

Mainly for the week content: large-scale machine learning, cases, summary(i) Random gradient descent methodIf there is a large-scale training set, the normal batch gradient descent method needs to calculate the sum of squares of errors across the entire training set, which is a very large computational cost if the learning method needs to iterate 20 times.First,

present, the common solution is to pre-edit the known sample points in advance to remove the small sample of the role of classification.5 Advantages and disadvantages of support vector machine (SVM)Advantages of SVM: * *One, can solve the problem of machine learning in the case of small samples.Second, can improve the generalization performance.Thirdly, we can s

Data preprocessing--building a good training data setThe ultimate learning outcome of machine learning algorithms depends on two main factors: the quality of the data and the amount of useful information contained in the data.Processing of missing dataIn practical applications, it is not uncommon for a sample to be missing one or more of the various reasons. The

At the time of learning the basic knowledge of machine learning, will read the contents of the book to remember, this blog code reference book machine learning in Action ("Robot Learning Combat").I. OverviewKNN algorithm is also c

machine learning consists of three stages : First stage: Learning model . Using the learning algorithm, the classification model is obtained by inductive learning of the training set. Phase two: test the Model . The classification models that have been learned a

Machine Learning: Decision Tree in python practice and decision tree in python practice Decision tree principle: Find the final feature from the dataset and iteratively divide the dataset until the data under a branch belongs to the same type or has traversed all the features of the partitioned dataset, stop the decision tree algorithm. Each time you divide a dataset, there are many features. How can we cho

In this lesson, we'll learn how to train a Naive Bayes classifier and a Logistic Regression Classifier-basic machine L Earning algorithms-on JSON text data, and classify it into categories.While the this dataset is still considered a small dataset – only a couple hundred points of data--we'll start to get Bette R results.The general rule was that the Logistic Regression would work better than Naive Bayes, and only if there was enough data. Since This

Http://www.csdn.net/article/2012-12-28/2813275-Support-Vector-Machineabsrtact: support vector Machine (SVM) has become a very popular algorithm. This paper mainly expounds how SVM works, and also gives some examples of using Python scikits library. As an algorithm for training machine learning, SVM can be used to solve classification and regression problems, and

classes in the data. - -Many, many more ... the the a total of 150 data samples the evenly distributed over 3 subspecies the 4 petals per sample, calyx shape Description - " " the the " " the 2 dividing the training set and the test set94 " " theX_train, X_test, y_train, y_test =train_test_split (Iris.data, the Iris.target, thetest_size=0.25,98Random_state=33) About - " "101 3 K Nearest Neighbor Classifier learning model and prediction102 " "10

About this article, my original blog address is located in http://blog.csdn.net/qq_37608890, this article from the author on December 06, 2017 18:06:30 written content (http://blog.csdn.net /qq_37608890/article/details/78731169). This article based on the recent Learning machine learning Books network articles, special will be some of the

, 1],[1, 0, 1, 2]])Another example: Np.random.randint (3, 6, size=[2,3]) returns data with a dimension of 2x3. The value range is [3,6].Array ([[4, 5, 3],[3, 4, 5]])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])

) Seeking a=x *θ (2) Ask E=g (A)-y(3) Request (A for step)3, algorithm optimization--stochastic gradient methodThe gradient rise (descent) algorithm needs to traverse the entire data set each time the regression coefficients are updated, which is good when dealing with about 100 datasets, but if there are billions of samples and thousands of features, the computational complexity of the method is too high. An improved method is to update the regressio

Blog has migrated to Marcovaldo's blog (http://marcovaldong.github.io/) The tenth lecture of Professor Geoffery Hinton, neuron Networks for machine learning, describes how to combine the model and further introduces the complete Bayesian approach from a practical point of view. Why it helps to combine models In this section, we discuss why you should combine many models when making predictions. Using multip

trees is simple (relative to the single decision Tree of C4.5), they are very powerful in combination.In recent years paper, such as ICCV this heavyweight meeting, ICCV 09 years of the inside of a lot of articles are related to the boosting and random forest. Model Combination + Decision tree-related algorithms have two basic forms-random forest and GBDT (Gradient Boost decision Tree), the other comparison of new model combinations + decision tree algorithms are derived from both of these algor