Questions tagged [scikit-learn]
A machine-learning library for Python. Use this tag for any on-topic question that (a) involves scikit-learn either as a critical part of the question or expected answer, & (b) is not just about how to use scikit-learn.
1,758
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Can SVM overfit even with cross-validation?
I am using SVM regressor models to fit some chemical data related to spectroscopy (I cannot say exactly what data because it is an ongoing research in my group). To combat overfitting, I have used 5-...
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202
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What's a good rule of thumb for choosing a sufficient number of quantiles in quantile transformation?
I'm a currently developing a regression model based on the Choquet Integral. To tackle outliers I am using the Quantile Transformer, provided by scikit-learn. I was wondering how the quantile number ...
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Data preparation (preprocessing and data cleaning) before or after train-test split with scikit learn?
I have been practicing Data Science/Machine Learning, and I am confused about when to complete the following tasks when using train-test split in scikit learn:
EDA
Filling in missing data
Removing ...
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Standardization on production dataset
Say I trained a Logistic regression model on a training dataset (sample = 2000), using Standardization. I than test the model on a test dataset (sample = 400) using the Standardization parameter ...
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Is it required to train the model in entire data after cross validation?
I have a model trained as follows.
...
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How can I add information about historical rates of occurrences to class probabilities?
first some introduction:
I have a trained a probabilistic softmax model using the sklearn multinomial logistic regression which predicts the class probabilities (class $A, ~B$ or $~C$) of any object ...
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31
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Understanding Stacked Generalization
I am trying to figure out how stacked generalization works? I think we train n models on the same dataset and get their class probabilities. Then these class probabilities are fed into another model. ...
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Reconstruct IR Spectra Based on PLS Model
I am currently using the scikit-learn package in python to setup PLS models (sklearn.cross_decomposition.PLSRegression) to predict the concentration of different substances based on IR spectra. In ...
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340
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F2 score or the Area under the Precision-Recall-Curve as a scoring metric
I have a dataset with which I want to perform binary classification.
The distribution of the target class is imbalanced: 20% positive labels, 80% negative labels.
The positive class is more important ...
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What are the main difference between a QQ plot and a probability plot for measuring nomality? [duplicate]
I am trying to evaluate the normality of the distribution of my model's residuals.
I have been using statsmodels.api.qqplot and ...
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Scikit-learn QuantileRegressor memory allocation error. No issue with statsmodel QuantReg with the same data
I'm trying to fit a quantile regression model to my input data. I would like to use sklearn, but I am getting a memory allocation error when I try to fit the model. The same data with the statsmodels ...
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How to properly impute values on the test set using imputer (missForest)
I'm trying to impute some missing values on my dataset $X$. So first I shuffle and split data to obatin the train set X_train and the test set ...
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How to transform prediction std of gaussian process back to origin
I am looking for a way of rescaling the predictions of my Gaussian Process Model back to the original scale.
The data is scaled for training using a ...
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What does it mean having 1 as best k parameter in K-NN?
I'm working with a large dataset (761 rows and about 57k-60k features) and after doing a feature selection to select the best 10 features I'm using different ML algorithms to classify some cases. In ...
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Hyperparameter tunning in SelectKBest feature selector
I am working with a pretty large dataset containing 760 rows and arround 58k-60k features and I'd like to perform a feature selection to reduce the dimensionality of those. After stardardising the ...
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Why are sklearn's cross_val_score values not increasing with the size of the training set?
I am working on a lithology identification project similar to the one described here.
The idea is to train a model using well log data collected at a handful of drillholes, in order to predict the &...
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266
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Combinatorial Cross-Validation Embargo and Purged
There is a library on GitHub called timeseriescv which implements Combinatorial CV. I am trying to use it in conjunction with GridSearchCV. However, unlike normal sklearn cross validators which have a ...
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Different precisions in predicting two classes with logistic regression
I am using the kaggle's stroke dataset trying to predict the stroke target feature, according to multiple predictive features. https://www.kaggle.com/datasets/...
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Train-Test Split with nested groups and multiple balancing factors
I have a large (~15,000) sample of data from individuals nested within families (with about half the data points sharing a family). I want to split the sample in to a training and test set so I can ...
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sklearn's permutation_importance returns surprising result
I have simulated normally distributed data (x_1 = np.random.normal(0, 1, size=1000)) and used it to create a dependent variable with a linear combination of the ...
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Defining a spatiotemporal (time-varying) kernel using GaussianProcessRegressor [closed]
(I edit the question to make it more specific and clear)
High-level idea
I want to implement the idea of capturing the correlation between two data points $x$ and $x'$ that can be different at two ...
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303
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Question about the output results of Scikit-learn's adjusted rand index
There is a problem that the calculation of ARI using the Adjusted_rand_score function in Scikit-learn does not match the results of the ARI calculation based on the paper proposed by Hubert et al1. ...
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Is there a stochastic AdaBoost?
In sklearn, the AdaBoostClassifier and AdaBoostRegressor classes do not have the subsample and max_features parameters, which are responsible for the stochastic approach to building a boosting model. ...
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XGBRegressor score (R2) vs. eval_metric (RMSE)
According to the API Reference, XGBRegressor().score() returns R2.
However, according to the XGBoost Paramters page, the default ...
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472
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Why does univariate Mahalanobis distance not match z-score?
I am using Mahalanobis distance for outlier detection. Sometimes my dataset only has 1 feature, sometimes many more. I believe the univariate Mahalanobis distance should be equal to the z-score of the ...
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196
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Random Forest Generating Bad Predictions: What might the issue be?
I'm using sklearn's RandomForestRegressor to try and model a relationship that involves three Feature variables (x1,x2,x3) and ...
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66
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Detect exact position of a word or number in a sentence with machine learning
I'm trying to come up with an ML model/s to detect if a sentence has sensitive data(telephone number, IBAN, address, etc.) and also get the position of said sensitive data.
For Example "My name ...
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How to step-into the learning process of support vector machines
OK, with the intention to produce a balanced class distribution for an imbalanced binary problem based on SVC, I created this custom function.
Basically, it takes binary imbalance data, fits an ...
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Using curve_fit for Non-Linear, Multi-Variate Models [Python] [closed]
Warning: ML Noob.
I have a 3D dataset (data at the bottom) with 2 feature variables and 1 target variable. Polynomial Regression produced unsatisfactory results and it seems that the relationship of ...
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Why would I get a non-zero value for mutual information for one variable, say x_m (belonging to (x_1, ..., x_n)), to target y, if x_m is a constant?
I have n 'features', (x_1, ..., x_n) and a target variable y. I use sklearn's mutual information score for feature selection to determine which features are the most important for the target y. I get ...
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Non-parametric equivalent of SelectKBest f_classif option
I'm doing a project where I am comparing various feature selection methods to see if they improve performance compared to the original dataset with 747 columns. One I want to try is the ANOVA method, ...
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308
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XGBoost Regression on a normal distribution variable produces a one sided distribution (only positive values)
I'm running a scikit-learn XGBoostRegressor with an RMSE loss function, on a variable with a distribution that is close to symmetric around 0 (think normal distribution, with a positive mean that ...
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245
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Duality gap calculation in Scikit-learn implementation of Lasso
I am writing a custom variation of Lasso regression, using sklearn's Lasso implementation as a "source of inspiration". And I don't quite understand the very last line in the calculation of ...
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195
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How to tune hyperparamters and use CalibratedClassifierCV correctly
Let's say that I have a classifier C ( for example a random forest classifier) and a Dataset.
From what I understand I can:
prefit the classifier on a portion of data and then "calibrate it"...
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97
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How does sklearn.tree.DecisionTreeRegressor work?
I have successfully trained the model on a dataset, but I have some questions because the documentation here is very difficult to read:
Does the splitter use a single scalar among the inputs, or ...
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How to find the optimal coefficients of the two predict_proba output matrices of two different classifiers using regression and maximizing accuracy? [closed]
I am performing classification, where there are six labels and two predict_proba (predicted probabilities) matrices as outputs. These two predict_proba matrices correspond to the outputs of two ...
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380
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Lasso regression prediction on test set is predicting towards the mean of the train set?
I am using lasso regression to predict age (continuous data) from a set having 2112 numeric features (indepedent variable).
The training dataset contains around 2773 participants. The mean of that ...
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In elastic net regularisation, will dividing the OLS term the number of observations cause misleading results when cross-validating?
Two formulations of the elastic net regression function
Consider sklearn's implementation of elastic net regularisation (Wikipedia link). From the docs, it works by ...
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149
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Scikit-learn's Gaussian Processes Classifier: Specific kernels for specific features [closed]
Using GaussianProcessClassifier from sklearn, is it possible to specify different kernels for different features? For example, $X$ is an n X 2 matrix and I would to use the RBF for the first column ...
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Number of samples in scikit-Learn cost function for Ridge/Lasso regression
I am using scikit-learn to train some regression models on data and noticed that the cost function for Lasso Regression is defined like this:
,
whereas the cost function for e.g. Ridge Regression is ...
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Difference K-fold versus Blocked Cross-Validation?
In the paper "Evaluating time series forecasting models: an empirical study
on performance estimation methods" by Cerqueira et al (2020), they mention k-fold cross-validation. Which they ...
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Stratification of the continuous y (target) variable in regression setting
Is it wise to stratify the continuous y (target) variable when you split your training and testing data from the total sample in regression setting? Here is the approach in python to do implement ...
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is nrmse scale-dependent?
Im trying to evaluate my regression models using a normalised version of the RMSE,
nrmse = rmse(y, y_pred)/rmse(y, y_mean) where y_mean is the array of the same len as y filled with the mean value of ...
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LinearRegression in Pytorch and sklearn, what is the differnece?
I am currently implementing Linear Regression in Pytorch and sklearn and I get two different Mean squared error (MSE) values for both. MSE is lower for Pytorch Linear Regression.
Wanted to ask what ...
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Avoiding data leakage in preprocessing and handling unseen values in test data
I've been reading up on avoiding data leakage in the preprocessing step of a machine-learning/data-science pipeline, specifically that it is wrong to apply preprocessing to both training and test data ...
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Using target values in both (X and y) arguments of fit(X, y)
This question is based on SQL Server Machine Learning Services Ski Rental tutorial.
We have a dataframe df:
...
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626
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Linear Regression with Lasso Regularization by using scikitlearn and scipy.optimize
i am trying to apply lasso linear regression with both scikitlearn and scipy.optimize min method.
However, i cannot reach same result. Code that i created with scipy.optimize can't shrink redundant ...
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301
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evaluating scoring metrics during hyperparameter tuning
I'm struggling with a couple of concepts related to hyperparameter tuning.
I'm developing a model (gradient boosted tree) in python using sklearn. Currently, I'm in the process of using sklearn's ...
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191
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class_weight='balanced' vs high f_beta score for imbalanced logistic regression in sklearn. Please help explain the difference
I have an imbalanced binary classification problem I am trying to solve with the LogisticRegression algorithm in sklearn.
As the data is highly imbalanced I am looking at ways to treat the imbalance ...
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1
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222
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Derivatives of output w.r.t input on a neural network trained with standardized data
I'm using a neural network to model an unknown function for which I would also like to know the derivatives. The nn has four inputs and four outputs, and the training data is preprocessed using scikit-...
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