It may be worth using the logistic function of the species: sigma(x) = 1/(1+exp(-x))

In examples, we see the following:out = model.predict(x) print(np.argmax(out)) if x There will be only one element, and the prediction will be for the only meaning, that's all.

This response is the translation of the user ' s reply. https://cs.stackexchange.com/users/755/d-w That's it. https://cs.stackexchange.com/questions/62978/what-method-of-collective-recogintion-to-use-for-digits-recognition in English and other experts in StackExchange https://stats.stackexchange.com/questions/232841/state-of-the-art-ensemble-learning-algorithm-in-pattern-recognition-tasks Cross Validated with similar language. User response https://cs.stackexchange.com/users/755/d-w That's it. https://cs.stackexchange.com/questions/62978/what-method-of-collective-recogintion-to-use-for-digits-recognition :Modern methods for numerical recognition do not imply use of collective recognition, competence areas, The ensembles or other algorithms mentioned on the question.Instead, modern methods to recognize numbers are used https://ru.wikipedia.org/wiki/%D0%A1%D0%B2%D1%91%D1%80%D1%82%D0%BE%D1%87%D0%BD%D0%B0%D1%8F_%D0%BD%D0%B5%D0%B9%D1%80%D0%BE%D0%BD%D0%BD%D0%B0%D1%8F_%D1%81%D0%B5%D1%82%D1%8C ♪ Only one stove neurosis: no need for use by several CNN or some other insulation. For this, emphasis is placed on the specific architecture of span neuronets (e.g., what sort of merger (sighs) https://ru.wikipedia.org/wiki/%D0%A1%D0%B2%D1%91%D1%80%D1%82%D0%BE%D1%87%D0%BD%D0%B0%D1%8F_%D0%BD%D0%B5%D0%B9%D1%80%D0%BE%D0%BD%D0%BD%D0%B0%D1%8F_%D1%81%D0%B5%D1%82%D1%8C#.D0.90.D1.80.D1.85.D0.B8.D1.82.D0.B5.D0.BA.D1.82.D1.83.D1.80.D0.B0_.D0.B8_.D0.BF.D1.80.D0.B8.D0.BD.D1.86.D0.B8.D0.BF_.D1.80.D0.B0.D0.B1.D0.BE.D1.82.D1.8B and learning procedures (e.g. adjustment School speed factor in the dental gradient method, (Resident) https://en.wikipedia.org/wiki/Dropout_(neural_networks) (b) " package " normalization (b)batch normalization) et al. As far as I know, it is also fair to recognize. objects/symbols.My advice is that the algorithms mentioned on the question look beautiful. on paper, they might not really be necessary or useful. Reality for the tasks you're interested in. I don't recommend using them. in practice.Part of reply https://stats.stackexchange.com/users/12359/franck-dernoncourt That's it. https://stats.stackexchange.com/questions/232841/state-of-the-art-ensemble-learning-algorithm-in-pattern-recognition-tasks :In one of the modern systems for the classification of images, there has been a tangible advantage in the use of ensemble (as in most other systems, as far as I know). Reflected in He Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun (Micsoft experts):Deep residual learning for image recognition/deep residual Image classification training (ENG, 2015, PDF)Tables are given in the article: Translation: Table 4. error factors (%) in results with Single Models (without ensemble) based on verification/control multis (validation set) Translation: Table 5. error factors (%) in results with ensemble♪ Top-5 error of metrology based on the ImageNet test multiplier.P. S. Top-5 error - a metric in which algorithm can give five versions of the painting class and error is counted if none of these options are correct.Note: explanation of what is top-1 error and top-5 error is also given here: https://stats.stackexchange.com/questions/156471/imagenet-what-is-top-1-and-top-5-error-rate MSRA team with their model ResNets They won the first competition. http://image-net.org/challenges/LSVRC/2015/results User response https://stats.stackexchange.com/users/80720/jean the same. https://stats.stackexchange.com/questions/232841/state-of-the-art-ensemble-learning-algorithm-in-pattern-recognition-tasks :I assume https://ru.wikipedia.org/wiki/%D0%93%D0%BB%D1%83%D0%B1%D0%B8%D0%BD%D0%BD%D0%BE%D0%B5_%D0%BE%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D0%B5 relevant. most tasks https://ru.wikipedia.org/wiki/%D0%9A%D0%BE%D0%BC%D0%BF%D1%8C%D1%8E%D1%82%D0%B5%D1%80%D0%BD%D0%BE%D0%B5_%D0%B7%D1%80%D0%B5%D0%BD%D0%B8%D0%B5 (decoration, Detection, extraction, allocation of borders, etc. specific tasks like https://ru.wikipedia.org/wiki/SLAM_(%D0%BC%D0%B5%D1%82%D0%BE%D0%B4) where deep education is not yet available He's getting to the current methods.Frequently, to some percentage advantage to win Recognition competition, use ensemble and Average♪ But the networks become so quality that it is. It doesn't affect the results that much.I'd say that in the next 20 years, most products. computer science will use in-depth training, even in that If there's something more effective. Adding to the answer. Franka, the algorithms of deep education change so fast that https://arxiv.org/abs/1512.03385 Kaiming He is no longer the most relevant For now. Right now. https://www.cs.toronto.edu/~kriz/cifar.html ♪ http://ufldl.stanford.edu/housenumbers/ in the light of the sofas:♪ https://arxiv.org/abs/1608.06993 and♪ https://arxiv.org/abs/1608.03983 ♪ But even these The results may soon cease to be relevant in the near future. http://image-net.org/challenges/LSVRC/2016/ ♪

Try changing:r2_score(Y, predictions) P_scores.append(r2_score) On:P_scores.append(r2_score(Y, predictions)) P_scores.append(r2_score) adds a reference to the function instead of the reset value(s)Demonstration:In [38]: y_true = [3, -0.5, 2, 7] In [39]: y_pred = [2.5, 0.0, 2, 8] In [40]: from sklearn.metrics import r2_score this reference to the function:In [41]: r2_score Out[41]: <function sklearn.metrics.regression.r2_score> and here we are getting the result of a function challenge with parameters:In [42]: r2_score(y_true, y_pred) Out[42]: 0.94860813704496794

What's stopping you from multiplying the score by one? PCA sets out the directions in the area, while the orientation of its own vectors seeking these directions does not play a special role.

It's easier and faster to show than to describe:In [62]: A = np.arange(25).reshape(5,5) In [63]: A Out[63]: array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19], [20, 21, 22, 23, 24]]) In [64]: A[:, 0] Out[64]: array([ 0, 5, 10, 15, 20]) In [65]: A[:, 0].shape Out[65]: (5,) In [66]: A[:, [0]] Out[66]: array([[ 0], [ 5], [10], [15], [20]]) In [67]: A[:, [0]].shape Out[67]: (5, 1) problem np.corrcoef() I think it's in my mind when computing the collateral matrix: np.cov() And it looks like it's a calculation. np.cov() For a matrix of the single column always produces a matrix consisting of all nan:In [149]: x = np.random.randint(0, 10, (5,1)) In [150]: x Out[150]: array([[4], [7], [3], [0], [0]]) In [151]: np.cov(x) c:\envs\py35\lib\site-packages\numpy\lib\function_base.py:2487: RuntimeWarning: Degrees of freedom <= 0 for slice warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning) Out[151]: array([[ nan, nan, nan, nan, nan], [ nan, nan, nan, nan, nan], [ nan, nan, nan, nan, nan], [ nan, nan, nan, nan, nan], [ nan, nan, nan, nan, nan]]) In [152]: x = np.random.randint(0, 10, (5,2)) In [153]: x Out[153]: array([[7, 0], [0, 8], [4, 2], [1, 5], [7, 1]]) In [154]: np.cov(x) Out[154]: array([[ 24.5, -28. , 7. , -14. , 21. ], [-28. , 32. , -8. , 16. , -24. ], [ 7. , -8. , 2. , -4. , 6. ], [-14. , 16. , -4. , 8. , -12. ], [ 21. , -24. , 6. , -12. , 18. ]])

Here table: (Cartine taken) https://infourok.ru/konspekt-uroka-po-russkomu-yaziku-na-temu-stili-rechi-klass-265374.html )As can be seen from it, the texts of different styles may predominate either terms, descriptions, treatments or slangs or other classes of words. Word classes will define neuronet after learning. She would, however, identify the predominance of a certain class or the combination of words in the text to be recognized. The structure of the text will also be among the signs (although it would be structured/unstructured). And then, neuronet teaching literature with backup and teacher training.The most important thing is... https://ru.wikipedia.org/wiki/%D0%9C%D0%B5%D1%82%D0%BE%D0%B4_%D0%BE%D0%B1%D1%80%D0%B0%D1%82%D0%BD%D0%BE%D0%B3%D0%BE_%D1%80%D0%B0%D1%81%D0%BF%D1%80%D0%BE%D1%81%D1%82%D1%80%D0%B0%D0%BD%D0%B5%D0%BD%D0%B8%D1%8F_%D0%BE%D1%88%D0%B8%D0%B1%D0%BA%D0%B8 ♪ (sighs) https://habrahabr.ru/post/154369/ - Habre.

(1) Classification It sounds kind of blurry. What kind of classes do you need? The mass options are spam/nespam, topical extraction, etc. A few examples of the original texts can even be provided.(2) Normalization and stemming of algorithms are similar but different. Normalization is the introduction of the word to the only number, the name paddle, the verb infinition, the present, etc. Stemming is a very rude operation that just separates the souffics and ends. But fast. Well, as it was correctly pointed out, the term words - excuses, interdiction of things - are removed before normalization. Maybe even the names of their own - even this step has something to think about.(3) If there is no first class sample, the task is very complicated. TF-IDF is simply a way to present the words as vectors - it will not be able to automatically extract any words that would characterize the proposal. TF-IDF doesn't have a parameter like "wear," word. (Chatters, the frequencies of the vegetation model will be multiple vectors, one vector for each unique word). There's only a frequency, and I'd be careful to say that a word describes the whole text or proposal based only on the frequency. So if there is no training sample, it's a dead end. And we need to look at other algorithms - LDA, LSI - they're capable of breaking many entry texts in categories, "themes," based on the contents of these texts. Patent status unknown. I didn't get the results of these algorithms. (4) If there's a learning sample, it's not easy either. It is not easy to take and teach the classification for variable length input data - in different documents or proposals different numbers of vectors. And all classic classifiers work with an inlet vector of the same length. There's also a mass to come up with, interpolate the missing values, fill the deserts, try to remove minor signs.Thus, a ready algorithm in the form of an "smoking" set of documents and everything has been done well. The closest LDA, LSI. And you can build hypotheses for a very long time if the task is not clear.What do you want? Since the language mark was not indicated, I dare to recommend writing it all on a python. There are already beautiful libraries for anything. gensim, NLTK for text work; skikit-learn, numpy, scipy, FANN, PyBrain, Theano for numbers; pymorhy2 for normalization of the Russian text, but normalization does not take place in context (this is not a way to distinguish the words from "stal."

Explanation:The Habre article last year, so suddenly it's outdated,This year, it has been recognized that the Windows project is inactive (see annex). https://github.com/dmlc/xgboost/issues/736 This is why a full CMake transition is planned. https://github.com/dmlc/xgboost/issues/792 )In mid-January, everything for Windows was removed from the repository.As long as it doesn't work out, you can take the old version of the repository, where the Windows are still eating. The references above show which audits are indicative.But the safest thing is to use the instructions. https://xgboost.readthedocs.org/en/latest/build.html#building-on-windows (on the reference https://github.com/dmlc/xgboost/issues/831 One nuance with the team. mkdirand the rest of it has to work.Supplement:I'm bringing down the download of the manual from the official website:First need to clone xgboost repositories. Open the git-shell and perform the following teams:git submodule init git submodule update After installation https://git-for-windows.github.io/ Let's go. Git Bash♪ All the following steps are being taken:MinGW for implementation make I need to call. mingw32-makeso we should add the following line to the file. .bashrc:alias make='mingw32-make' To collect xgboost at MinGW, perform the following team:cp make/mingw64.mk config.mk; make -j4 Supplement 2:Answer to the question of how to open git-shell:If http://windows.github.com/ it was supposed to be such a label. If he's not here for some reason, you can start a git-shell by one of those teams (depending on the case, don't forget to put the right thing in. UsernameC:\Users\Username\AppData\Local\GitHub\GitHub.appref-ms --open-shell orC:\Users\Username\AppData\Local\Apps\2.0\GitHub\GitHub.appref-ms --open-shell Open the git-shell of one of these crews, re-establish the necessary labels by performing the team.github --reinstall-shortcuts

It appears to be a combination of caring algorithms. What's in English called ensemble classifiers.The main idea is that one head is good and two (or more) is better. If you have a few classifiers whose mistakes are statistically independent, their efforts can improve the accuracy of the classification. The most common versions of classifiers are baggering (bagging) and bushing (boosting).

A small abundance of data has been created, which, in the task, requires an extension of the linear function. Po https://ru.wikipedia.org/wiki/%D0%9C%D0%B5%D1%82%D0%BE%D0%B4_%D0%BD%D0%B0%D0%B8%D0%BC%D0%B5%D0%BD%D1%8C%D1%88%D0%B8%D1%85_%D0%BA%D0%B2%D0%B0%D0%B4%D1%80%D0%B0%D1%82%D0%BE%D0%B2 dependency yi = a xi + b It's not difficult to get a SLAW of the species.s2a + s1b = r1♪s1a + s0b = r0♪wheres0 = sum(i)0) = ns1 = sum(i)1) = n(n-1)/2s2 = sum(i)2) = n(n-1)(2n-1)/ 6,r0 = sum(i)0xi) = sum(x)i),r0 = sum(i)1xi) = sum(ix)i),The solution is simple.Programme:$issue = [10, 40, 72, 99, 164, 172, 190]; function line($arr){ $r0 = array_sum($arr); $r1 = 0; foreach($arr as $key=>$item){ $r1 += $key*$item; } $s0 = count($arr); $s1 = $s0*($s0-1)/2; $s2 = $s1*(2*$s0-1)/3; $delta = $s0*$s2 - $s1*$s1; $delta1 = $s0*$r1 - $s1*$r0; $delta2 = $s2*$r0 - $s1*$r1; $a = $delta1/$delta; $b = $delta2/$delta; return [$a, $b]; } list($a, $b) = line($issue); printf("y = %.3f x + %.3f b", $a, $b); Result:y = 32.000 x + 10.714 b

What if I come on the other side? First teach a lot of trees and then build a schedule?import numpy as np from sklearn.metrics import mean_squared_error as mse def get_mse_list(X, y, clf): predictions = list() mses = list() for i, tree in enumerate(clf.estimators_): predictions.append(tree.predict(X)) rf_prediction = np.mean(predictions, axis=0) mses.append(mse(y, rf_prediction)) return mses

This is a function that solves the problem.my.strata <- function(v) { tmp <- as.vector(table(v)); num_clases <- length(tmp); min_size <- tmp[order(tmp,decreasing=FALSE)[1]]; rep(min_size,num_clases); } randomForest(.... sampsize=my.strata( ) ... )

It's a cluster algorithm. http://www.machinelearning.ru/wiki/index.php?title=%D0%90%D0%BB%D0%B3%D0%BE%D1%80%D0%B8%D1%82%D0%BC_%D0%A4%D0%BE%D1%80%D0%AD%D0%BB

You already have a matrix of distances to cluster centrifugesuges - more simple work with matrices.Decision algorithm:selected from the matrix of distances only the line belonging to the cluster 0Consider the average value (function/method) mean() In the previous step matrix in the first column (indicator table) 0) I'm out of the way the distance to the cluster is. 0 is located in the first pillar of the distance matrix.

We could use some kind of notebook. https://www.kaggle.com/ ♪ Like this one: https://www.kaggle.com/stpeteishii/lfw-people-conv2d

I understand there are endless solutions to this task unless there are any additional restrictions on these unknown two sets. The only reasonable option here is to forget about two unknown masses and simply consider the meaning of the function as a time series. There are many ways to predict the time series, but there is a need for a preliminary analysis of the data - to see if there is any repeatability in the data, etc.Or maybe you have the data so linear that their normal linear regression from a number of values. x I can predict. But without seeing the data, you can't say anything.

If I understand correctly, you're just making a second on the numbers.For Pandas DataFrame:test_filter = inputs['col2'] < 4 inputs_test = inputs.loc[test_filter] inputs_train = inputs.loc[~test_filter] For Numpy array:condition = arr[:,1] < 4 inputs_test = inputs[condition] inputs_train = inputs[~condition] On the question from the comment:condition = np.isin(arr[:,1], (4, 6))

Krch, the problem was that somewhere on the dateset was the meaning. nan

Any model of machine training/non-tural networks always expects data in a given format or, in technical language, a tensor of the same size, not taking into account the first or last axle (number of samples). For example, if you have a data set of 2D matrix or table, the model will always expect the same number of columns. The number of lines may vary.Data outside the sample is the data you'll get at the entrance. future years♪ In order to launch and test such a model, the following are done:A fairly large sample of data is collectedsplit into two parts - training and test sampleIn the case of a time series, the last 20 to 30 per cent of the data are usually taken as a test sample. If this is not a time series, then the data are intermittently mixed and divided into two parts - 70 to 80 per cent for training, the rest for testing.We further learn the model on the training set, and the accuracy of the model is checked on the test set.When we're satisfied with the results of the tests, the model rolls out into the market and starts working on completely new data. Of course, all the pre-processing (pre-processing) steps you've done for the study and test sample should be repeated for new input data.after this, its model is usually supplemented by new data.That's it. ♪ ♪