машинное-обучение

I'm taking a course in data science and they use the sklearn library, where there is a GridSearchCV method, the problem is th ... ain_fin, y_train_fin) sorted(gridsearch.cv_results_,key = lambda x: -x.mean_validation_score) What could be the problem ?

For example, a feature that takes the values {'Male', 'Female', NaN}, when using OneHotEncoder (or some other means), transla ... such a dataset should be obtained regardless of whether the set on which the encoder was trained had Nan in some categories.

I output the calculated metrics for the test data: print(classification_report(y_true, y_pred_classes, target_names = CLAS ... 0.92 0.92 0.92 10000 I'm missing micro avg in this report. How do I correct the output to add this line?

The task is to normalize (0-1) the order stack in stock trading. In the glass, I can only see the 25 best price offers, i.e. ... base for normalization? And if so, how?(Orders are quickly canceled/appear and it is not clear what data should be recorded)

How can I use sklearn or numpy to find the fraction of wrongly predicted values? There are two arrays of numbers of the same ... d to compare them, and divide the number of incorrect answers by the length. Is it possible to do this somehow in 1 function?

Now I am engaged in machine learning, can someone tell me in detail why the MO needs X_train,X_test, y_train,y_test, the argu ... X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42) And how is the test_size parameter set?

I select the parameters for the best training of the classification model. I do it like this: print('Исходная обученность: ... ий: ', accuracy_score(res3, y_test3)) As a result, the results are different. Where did I go wrong? What am I doing wrong?

There is a certain classification task: for training, the classifier receives an array of strings as a class and some numbers ... the problem, because I don't know about methods with multiple classes for an object yet. I will be grateful for your advice!

Tell me, I have a df... If I'm going to use cross-validation, it's enough for me to split my df into training and test samples and I don't need to extract the validation set additionally. Right? Or do I not understand something correctly?

Hello everyone. I continue to slowly study ML and got to the well-known data set 'Wine'. And I hit the next point: if I use e ... rong (for example, I calculated the accuracy)? I read the theory and did not find any prerequisites for such big differences.

Here is the code from the course y_pred_train=logreg.predict(x_train) y_predict_train=logreg.predict_proba(x_train)[:,1] lo ... that is, how many times do not call predict (), the coefficients or weights will be the same. But I'm not so sure about that)

import pandas as pd import numpy as np from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import tr ... an error. ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

There is such a code: from keras.models import Sequential from keras.layers import Dense import numpy from numpy import exp, ... (should be: 0, 1): For ReLU: accuracy: 50.00% [[0.9953842]] [[0]] For Sigmoid: accuracy: 100.00% [[0.13338517]] [[1]]

What do these concepts have in common and how do they differ? Do I understand correctly that with L1 regularization, some o ... not "fade" because of one feature? And L1 and L2 are the norm, are they just different options for calculating the distance?

I need to classify texts (news) by the importance of the Russian Federation. 1 category-threat of NB (key phrases: terrorism, ... out myself. But I have little experience, and I can't figure out how to fill it out myself, without delivering a set of texts

I'm new to python and don't quite understand how I can calculate the average relative approximation error using the formula ... ready-made function for getting this error, or is it just a loop? If it is a loop, do I need to normalize Y_test-real values?

After all, even if the values of the activation function were in the values from -10 to 10, this would make the network more ... ble, as it seems to me. After all, the problem can't just be the lack of a suitable formula. Please explain what I'm missing.

I am implementing a linear regression algorithm based on two parameters. When the dataset is increased by an order of magnitu ... r(x, y) plt.plot(*zip(x, y), marker='o', color="r", ls="") plt.plot([t0 + t1 * i for i in range(10)]) plt.show()

There is a scan of the document in pdf format. How to recognize text in a certain area of such a document, more precisely, a digital number?

Tell me if it is possible and how you can add a condition so that the prediction of the label knn.predict(x_test) occurs only ... _score(knn, X, Y, cv=LeaveOneOut()) print(scores.mean()) Or is there another method more suitable for this purpose? Thanks!