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.
2,304 questions
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Qiskit Problem: this solution is a bit slow, is there a way to make it faster and increase the accuracy a little bit?
I'm currently making a small binary classification program using Quantum Machine Learning (EstimatorQNN to be more specific). My program classifies data inside the Wisconsin Breast Cancer database and ...
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Sklearn ROC Curve not square
I am using sklearn.metrics.roc_curve to calculate the points of a ROC curve.
This is the output I obtain.
This plot does not look as I would expect it to. The line ...
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Principal Data Analysis - how to determine the key features contribute to PC1 using scikit-learn python
I struggle to select the key features that contribute to PC1. I will use the public breast cancer dataset to illustrate the issue. Please feel free to point me to previous post if this question has ...
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Runtime complexity of scikit-learn’s One-vs-Rest LogisticRegression (LBFGS) vs. RidgeClassifier
I’m working through the runtime analysis of scikit-learn’s OneVsRestClassifier for two cases:
LogisticRegression (solver=lbfgs, ...
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Sklearn's One-hot encoder adds an extra column for NaNs which are not there
I would appreciate your advice on how to resolve the following issue.
I am working with a dataset that contains two categorical features (actually, more than two, but two are enough to illustrate the ...
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expected the model to forecast resolution time more accurately based on past ticket patterns. I was also hoping to unde
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I want to build a model that forecasts ticket resolution time for a data science software support tickets . I’ve calculated queuing time and resolution time from ...
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Clarification about scale dataset for MLP regression model and use of the scaling inverse transform
I am a lot confused about the pre-processing scaling process.
I have a dataset with several meteorological quantities (pressure, temperature, wind direction, etc.) and I am using it to forecast the ...
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How to build model with smoothness via various data point
I am trying to model the arch of a basketball free throw projectory. Usually per person, this dataset has 6 points each where it is the height of the basketball via various seconds after the player ...
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Nested cross-validation: which implementation to use? different purpose?
I am learning Machine Learning and exploring nested cross-validation. I don't understand the example given in scikit-learn as the model seems to learn from the whole dataset and the evaluation is not ...
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Much higher scoring metrics with classification_report than cross_validate
I'm training a classifier on the DAIGT dataset. The objective is to differentiate human from AI text and so this is a binary classification problem. As a baseline before I move onto an LLM classifier, ...
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Loan prediction model relying almost entirely on Credit_History and ignoring other features
I'm building a machine learning model to predict loan approval rate. My dataset includes features like:
Credit_History
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"Singular values of x" in LinearRegression
LinearRegression has an attribute singular_ which returns "singular values of x". According to a definition I found: "singularity is ... when a ...
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Why is DecisionTree using same feature and same condition twice
When trying to fit scikit-learn DecisionTreeClassifier on my data, I am observing some weird behavior.
x[54] (a boolan feature) ...
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Low Accuracy from Geospatial Random forest ML modeling problem - Training Exported from qGIS, SCP
I am doing a geospatial assessment integrated with ML modeling. The problem is the very low accuracy percentage, as more training features increases, it gets lower. What could be the solution to such ...
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Isolation Forest sample size
I am using sklearn's Isolation Forest as a model to detect anomalies. My dataset is relatively small, 50 records with only 2-3 features.
To prevent any overfitting, what would you recommend to tune ...
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