sklearn

Hi, everybody, I have an algorithm: clf = LGBMClassifier() clf.fit(X_train, y_train) When I use this algorithm to predict ... odel)? I.e. for example, I want that when treshold>=0.6, class 1 was predicted, and class 2 was predicted for treshold fit

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?

I need to normalize data I have so that it is between -1 and 1. I used StandardScaler, but the range got bigger. What other ... caler = StandardScaler() scaler.fit(df) dftrans = scaler.transform(df) plt.plot(dftrans) The die is between -10 and 10.

From what I've researched, both divide the set into two training and test subsets. Is there any difference between the two?

The code below I found in the link Classifying the Iris Data Set with Keras . And I would like to understand what the utilit ... , Y_test = train_test_split( X_scaled, Y, test_size=0.5, random_state=2) n_features = X.shape[1] n_classes = Y.shape[1]

Model: logistic regression with sklearn. I decided to compare the results shown in classification_report, calculating them u ... n see, recall and precision do not beat. What's wrong? How to interpret the results? What do f1-score and support stand for?

I'm trying to learn how to fit a quadratic regression model. The dataset can be downloaded at: https://filebin.net/ztr9har5n ... .iloc[:,0] ## How to fit a quadratic regression model using sklearn and statsmodels ? I I can only use linear regression...

I'm trying to use the scikit-learn library RFE in models I created using tensorflow, but when I try to train I get TypeError: ... Continuum\anaconda3\lib\copy.py", line 169, in deepcopy rv = reductor(4) TypeError: can't pickle _thread._local objects