I try to train and test several scikit-learn models and attempt to print off the accuracy. Only some of these models work, others fail with th
I use a CatBoostClassifier and my classes are highly imbalanced. I applied a scale_pos_weight parameter to account for that. While training with an evaluation d
When using the scikit-learn library in Python, I can use the CountVectorizer to create ngrams of a desired length (e.g. 2 words) like so: from sklearn.metrics.
I want to get the coefficients of my sklearn polynomial regression model in Python so I can write the equation elsewhere.. i.e. ax1^2 + ax + bx2^2 + bx2 + c I'
I'm trying to implement SMOTENC inside a column transformer. However I'm getting error. The code and the error is provided below. #Create a mask for categorical
I have a dataset with 7 labels in the target variable. X = data.drop('target', axis=1) Y = data['target'] Y.unique() array(['Normal_Weight', 'Overweight_Level_
I am trying to determine roc_auc_score for a fit model on a validation set. I am seeing some conflicting information on function inputs. Documentation says: "y_
Usually, we apply cross_val_score to the Sklearn models by doing the following way. scores = cross_val_score(clf, X, y, cv=5, scoring='f1_macro') Now I have my
I am using truncated SVD from scikit-learn package. In the definition of SVD, an original matrix A is approxmated as a product A ≈ UΣV* where
I have a series like: df['ID'] = ['ABC123', 'IDF345', ...] I'm using scikit's LabelEncoder to convert it to numerical values to be fed into the RandomForestC
I have a dataset that consists of images and associated descriptions. I've split these into two separate datasets with their own classifiers (visual and textual
I have a dataset that goes from 2016 to 2020 with a 'Year' column. I would like to use 2016-2017 as train data and 2018-2020 as test data. Is there any easy met
I'm trying to create a uniform distribution between two numbers (lower bound and upper bound) in order to feed it to sklearn's ParameterSampler. I am using scip
I have a large data-set (I can't fit entire data on memory). I want to fit a GMM on this data set. Can I use GMM.fit() (sklearn.mixture.GMM) repeatedly on min
I have a dataframe of few hundreds rows , that can be grouped to ids as follows: df = Val1 Val2 Val3 Id 2 2 8 b 1 2 3 a 5
From the documentation scikit-learn implements SVC, NuSVC and LinearSVC which are classes capable of performing multi-class classification on a dataset. By the
I am trying to train a model, but I am getting this error Input contains NaN, infinity or a value too large for dtype('float64'). Here's part of my code, how
Hey I'm new to Python and I am trying to follow along with a tutorial but I get this error: NameError: name 'tree' is not defined. The objective is obvio
I'd like to use the warm_start parameter to add training data to my random forest classifier. I expected it to be used like this: clf = RandomForestClassifier(
i am running the below code and getting this error. Please help: Error: NameError: name 'predictions' is not defined Code: import pandas as pd import numpy a
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