Questions tagged [scikit-learn]
scikit-learn is a popular machine learning package for Python that has simple and efficient tools for predictive data analysis. Topics include classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.
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Do you have to use clustering with SciKit-Learn's Mutual Information metric?
I'd like to calculate the mutual information between two datasets, but I'd prefer not to cluster them first.
I'm thinking of using SciKit-Learn's mutual_info_score ...
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How to boost the performance of a single decision tree by adding additional trees?
I have a binary classification task and the data has imbalance issue (99% is negative and 1% is positive). I am able to build a decision tree that is carefully tuned, weighted, and post-pruned. Take ...
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Difference between Validation Error on Learning Curve and Validation Error Calculation in Machine Learning Model
I am encountering a problem where the validation error I see on the learning curve of my machine learning model is different from the validation error I calculate using the mean squared error function....
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How to add query filter to the Nearest Neighbors algorithm?
I have Nearest Neighbors model, built with sklearn sklearn.neighbors.NearestNeighbors, which I use to make content based recommendations.
Sometimes I need to ...
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SequentialFeatureSelector and scoring for clustering
I want to use SequentialFeatureSelector from scikit-learn to do feature selection for clustering (K-Means). [Here][1] is a list of available evaluation scores for clustering. What I do not understand ...
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using a feature that is only available during training
I'm working on a project that aims to classify JIRA issues into their relevant owner group.
An issue has the following text features:
Summary
Description
Comments
all of which are text based.
During ...
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What's the fastest clustering package in Python?
I'd like to perform clustering analysis on a dataset with 1,300 columns and 500,000 rows.
I've seen that clustering algorithms are available in SciKit-Learn. But I'm worried that the algorithms will ...
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Sklearn predicts different results depending on the input length
Here is the problem: I fitted a Random Forest Classifier and saved it to a pickle file. However, when I predict with the entire dataset I get one result, and when run predict line by line (loop) I get ...
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passing pandas dataframe to function, but it appears as numpy array
In the function I call sklearn train_test_split
In Pycharm debugger everything looks ok until it gets to that call.
Then it backtracks and tells me
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Teaching the model on more than one dataframe/dataset
I am trying to write a thesis on oil pipe leakage detection. The aim is to predict the size and location of the leak. My problem is that I can only run single simulations using a software called OLGA ...
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Can BM25 be used as an embedding algorithm?
I'v studied about BM25 algorithm. Untill now, I couldn't find an implementation of BM25 to give me an embedding of a text like TfidfTransformer and ...
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What else can I do to help my model my classification task?
I have a classification task that I'm currently getting really low accuracy metrics on (my highest accuracy score is about 20%). So far I've run 5 models: quadratic disc analysis, logistic regression, ...
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Tuned model has higher CV accuracy, but a lower test accuracy. Should I use the tuned or untuned model?
I am working on a classification problem using Sci Kit Learn and am confused on how to properly tune hyper parameters to get the "best" model.
Before any tuning, my logistic regression ...
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I am getting all scores as 100% on my machine learning models. Is it okay to have this kind of result?
I am getting all scores for my ML model as 100% for the Extra Trees Algorithm. I am applying the necessary pre-processing steps (duplication removal, correlations validating, cardinality validation, ...
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Do we need to check the training score when we use randomizedsearchcv
given a model and a set of parameters, randomizedsearchCV(or gridsearchCV) gives the mean of the best scores from a list of different folds of the datasets.
Does the model control for overfitting? I ...
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how do I test if overfitting exists when I use cross_val_score method?
I got the following code form a book on xgboost.
I wonder whether this is a correct way of analyzing cross validation score for overfitting purposes.
mean accuracy is 81 which can be okay. but what if ...
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How can I be more certain that I have not accidentally made my ML model predict on training data?
I have this random forest model setup as shown below in python. It's performing unexpectedly well with a ~70% classification success rate (to the extent where I really doubt it is genuine) and I am ...
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Ugly AUC curves. Sklearn. How to make AUC Curves less square
I dislike the square look of this AUC curve (SKLearn).
The purpose of this question is "visual". Please post code snippets.
This question is not requesting the theory behind the AUC.
My goal ...
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How to determine which combinations of parameters to include in GridSearchCV
I am using MLPClassifier from sklearn and I would like to tune it with GridSearchCV. But I don't know which set of values to include for hidden_layer_sizes, max_iter, activation, solver, etc. How can ...
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Maximum categories for categorical variables in K-Means clustering
I am trying to perform K-means clustering on a dataset, and one of my categorical features has 96 possible options. Would this be too many features for one variable to have? The alternative would be ...
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Imputation of missing values based on target variable
I want to impute missing values in German Credit Risk dataset.
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Surrogate splits in Python
I want to use RandomForestClassifier from Sklearn to predict categorical variable (credit risk). But one of the predictors seems to have missing values:
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Can I use a fitted polynomial regression to make reverse predictions?
I want to start off by acknowledging that this may be a dumb-sounding question to someone with more machine learning experience to me, so please go easy. Here is the background. I am currently an ...
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Does OpenAI and ChatGPT use Scikit Learn?
Scikit Learn is Python's go-to open source package for running common AI and machine learning algorithms. Does OpenAI and its product ChatGPT use or rely on Scikit Learn on its back-end at all? If not,...
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ValueError: Found input variables with inconsistent numbers of samples: [120, 30]
I practice XGBClassifier() to predict the target in iris dataset.
here is the code:
...
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How to make a predictive model using a timeseries data consisted of binary information?
I have a set of data that is showing the state of an object as a function of time. I would like to know what and how I should be utilizing machine learning to predict the state of the object at some ...
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high degree polynomial model with sklearn does not fit
The idea was to gradually raise the degree of the polynomial. Here is the code that implements creating a random dataset, fitting the polynomial of the CHANGE_ME ...
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Classifier calibration leading to worse outcome
I am trying to calibrate some classifiers to output more accurate probabilities. For this, I am using a sigmoid regression as implemented in ...
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Get multiple predictions from a knn model
I want to my code to return multiple(5) predictions from my trained knn model. I've tried using predict_proba() but it just returns the probabilities and not the names
Here is my code:
...
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sklearn.model_selection cross_validate accuracy parameter of score set to NAN
Problem
On sklearn.model_selection import cross_validate the accuracy parameter of score set to NAN.
working with score_metrics to collect all accuracy and error measures from cross_validate, sklearn.
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How to structure dataframe combinations for regression, without corruption/loss?
I have a data set, redacted sample below. My goal is linear regression. My question is: Have I created unintended results, due to how I structured the df, using concat and/or div?
For example, ...
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Sklearn pipelines, applying transformations to feature engineered columns in previous steps
I am working on a sklearn pipeline for my binary classification problem. The pipeline should perform typical things like downcasting data types, scaling numerical values, one-hot and ordinal encoding ...
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problem with imputing in sklearn
I have used SimpleImputer() to fill the missing values
my_imputer = SimpleImputer()
data_with_imputed_values = my_imputer.fit_transform(train_data)
what I expect ...
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What is Auto-Sklearn Dummy Model?
When I apply AutoSklearn for some datasets I get this error
No models better than random - using Dummy Score
What does it mean? what is this Dummy Model?
How does ...
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problem with standardScaler
problem with standardScaler
hi
I'd like to scale one column in the titanic data set. I am using the following code segment.
for some reason df_scaled results an empty set.
how can I solve it? what is ...
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Multi-label predictive modeling using Naive Bayes
I'm working on a predictive model that currently uses scikit-learn's Naive Bayes (testing with both Bernoulli and Gaussian) but I'm running into some trouble.
First I have a large dataset of records (...
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scikit learn logistic regression to annotate single cell RNA seq data
I want to use scikit learn logistic regression to train a model on a labelled single cell RNA sample and subsequently apply this model on new unlabelled single cell RNA seq samples to annotate the ...
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Building recursive forecasting from scratch?
I am currently testing time-series modelling with tree-based (xgb, lgbm, cat) algorithms using, recursive lags of a y (sales) along with with numerous exogenous regressors i.e. date-time, price, ...
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Boosting the effect of some of the features in SVM
I'm doing text classification with SVM. I'm using Tfidf vectorization. In addition to the text vectors, I have a context data denoting the possible outcomes of the prediction. For example, I have a ...
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DecisionTreeClassifier cannot take one-hot encoded classes?
I got ValueError: Found array with dim 3. None expected <= 2. I dont know which array has dim 3?
DecisionTreeClassifier cannot take one-hot encoded classes?
But ...
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What is the difference between nmf.fit() nmf.fit_transform() in a easy way?
I am reading several questions on this topic.
It seems quite clear to me for TFIDF why we have .fit_transform() and .transform()
...
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From features to sequence
I want to train a supervised algorithm capable to rebuild from features (like mean, std, feature from frequency domain etc.) the original signal, this signals is discrete and have different lengths. ...
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Random Forest with less samples & variation in test_scores
I'm building a RandomForestRegressor with 75 samples. The distribution of y (After train_test_split) is as below. (Blue-Train and Red-Test)
Keeping test_size=0.3 (...
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How to remove dataset line inside sklearn Pipeline
I am using a custom transformer inside sklearn pipeline. The transformer removes lines from data set, but it seems it can only remove the lines from X, but not from y.
I see the pipeline automatically ...
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How recursive feature elimination with cross validation internally works?
I am trying to understand how recursive feature elimination with cross validation works (the RFECV on sklearn). Lets say that we have 10 features, and we perform <...
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What is the difference between the CCA weights and rotations?
I have been looking at the scikit learn Canonical Correlation Analysis (CCA) algorithm, and I have come across the terms "weights" and "rotations" as parameters of the CCA model. ...
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Does sklearn perform feature selection within cross validation?
I would like to add a feature selector on my pipeline and use gridsearchcv to tune both the hyperparameters of the selector and the classifier(s).
I am wondering if sklearn performs feature selection ...
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How does GridSearchCV use Cross Validation to produce a Model's Score?
I understand Cross Validation in practice, but I'm not sure how SciKit-Learn's GridSearchCV uses it to produce an overall score/ metric for a model. For example, if ...
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How can I use XGBoost in SciKit-Learn on an Air-gapped Computer?
I'd like to use XGBoost in SciKit-Learn, however, I'm on an Air-Gapped computer and can't install it normally using pip.
How can I install XGBoost on an air-gapped computer?
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