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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Feature Selection algorithm/library for CRF
I am using the Conditional Random Fields CRF suite scikit-learn wrapper algorithm. I have read on the literature various approaches for feature selection, but I cannot find any on that package or, ...
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Naive Bayes / SVM classifiation - min. number of records (Python)
I am doing text classification with Python. I have around 120 records with 2 columns:
text
class
I tokenize, stem and lematize the words, I also did some of my own text preprocessing. When I run the ...
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ValueError: Expected 2D array, got 1D array instead
I would like to extract the 20 most informative features of a very large set of features $X$ coming from a dataset containing clinical data by using the RFE function from scikit-learn in Python.
$X$ ...
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Managing NaN in target variables (testing)
Please can someone advise me on how to handle NaN in my target variables set?
I've tried a variety of things but none is working. Here's what I've tried:
Imputing zeros (0) in Y_test
Replacing NaN ...
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How to encode an array of categories to feed into sklearn
I'm working on a recommendation problem, broadly following the Youtube paper on theirs. Their surrogate problem is to recommend the next video a user will watch. One feature they include in their ...
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Regression Task - Spark, PyTorch, TensorFlow or scikit
I know it's a broad question, sorry for that, but I'm still testing the waters with machine learning.
I have a typical regression task (predict target numbers with the help of features x,y,z) and a ...
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How to combine GridSearchCV with Early Stopping?
I'm a beginner in machine learning and want to train a CNN (for image recognition) with optimized hyperparameter like dropout rate, learning rate and number of epochs.
The optimal hyperparameter I ...
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Is it possible to know the output vectors of MLP Classifier of scikit learn?
I'm a beginner with scikiti-learn library.
I have an ANN with 3 input, 2 hidden layers and 3 output.
...
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sklearn.feature_selection vs xgboost feature_importances?
sklearn.feature_selection vs xgboost feature_importances
Can somebody explain in-detailed differences between sklearn.feature_selection and xgboost feature_importances? And how the algorithms work ...
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Why are my Decision Tree Leafs not pure?
I'm making a using DecisionTreeClassifier from SKlearn (v0.21.3) with its default settings, using Python. I do not want regularize it in any way, I want it to ...
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GridSearchCV vs RandomSearchCV and How it works?
GridSearchCV vs RandomSearchCV
Can somebody explain in-detailed differences between GridSearchCV and RandomSearchCV?
And how the algorithms work under the hood?
As per my understanding from the ...
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SGDClassifier partial_fit() for online learning - is one step of gradient descent enough?
I'm interested in incremental (online) learning for my logistic regression model trained with SGDClassifier. Basically updating the model as more labeled data comes ...
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Is there any optimal way on feature selection for more than one classification algorithms?
I have a wine dataset with 13 features that indicates 3 different wine classes (target), and k-NN, SVM with linear kernel and SVM with rbf kernel algorithms to be tried with this dataset.
My goal is ...
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What is the scikit learn Non-negative Matrix Factorisation Coordinate Descent algorithm?
What is the scikit-learn Coordinate Descent (CD) algorithm for Non-negative Matrix Factorization (NMF)?
The sklearn implementation of NMF has two different solvers, Coordinate Descent and ...
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Testing if a sample fits into an existing cluster
I have a sample of data I'd like to create a model from, which would create N clusters.
After the fitting to clusters, I'd like to test various samples against the existing clusters, seeing if the ...
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Permutation feature importance vs. RandomForest feature importance
What is the difference between Permutation feature importance vs. RandomForest feature importance? What are the disadvantages vs. advantages of the two techniques?
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Using a trained classifier in an Android app
As the title suggests, I'm attempting to train some different classifiers into an android app. The main question I have is how to represent the different models in a neat and effective way, from ...
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How to interpret ANOVA results?
I am trying to identify what attributes are not relevant in my dataset to remove them before fitting a classifier.
The target is a categorical variable with three different values.
I also have a lot ...
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Need machine learning algorithm to fill in time-series data
I am currently dealing with a time-series data set with cyclical gaps every 30 minutes (30 minutes of data, 30 minutes of no data). Is there a relatively simple way of using scikit-learn (or some ...
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Linear Regression on data with bimodal outcome
I have a data set with 3,000 features and continuous dependent variables of time with 18,000 instances. The histogram of the dependent variables show that the they have a bimodal distribution. I am ...
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TypeError: 'GridSearchCV' object is not callable - how do I use a pickle of an SVM (Scikit-learn)?
I have created an SVM in Scikit-learn for classification. It works; it prints out either 1 or 0 depending on the class. I converted it to a pickle file and tried to use it, but I am receiving this ...
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Training a model sample by sample
I'm training a Scikit model but it seems that in all examples, they call the fit method on the entire training set. What I want to do however is call it per sample (...
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Increase accuracy of classification problem [closed]
I am trying to build a classifier that predicts the compiler given some operations of assembly code. Here is the pandas dataframe:
What I do is using a TfidfVectorizer and select the features that ...
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How do I force specified coefficients in a Linear Regression model to be positive?
Looking for a way to do this in Python. scipy.optimize.nnls forces all coefficients to be positive.
Some additional context: I have a data frame with a some explanatory variables and a response ...
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can i get weights per iteration of MLP?
im building an mlp with scikit learn. Is there a way I can access weights and biases of the output layer per iteration? There is an option
mlp.coefs_ But it ...
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Applying Standardization OLS estimator
I have basic understanding of how to perform linear regression with sklearn and statsmodels. There are several questions that I would like to ask regarding Linear Regression (OLS estimator) :
Is ...
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Building an efficient feature vector
I am building a classifier for malware analysis, which predicts if I have a malware by looking at the intructions of an assembly code, such as push, mov,... and predicting the optimization method. ...
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Scikit model is not able to predict sequence correctly
I am trying to create a regression model using scikit-learn for predicting car price. The input data are, car model(trim), kilometers used, past resale price of similar car and age of used car. I am ...
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Difference between learning_curve and validation_curve
What is the difference between these two curves: learning_curve and validation_curve ?
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How to Keep Missing Values in Ordinal Logistic Regression
I’m using mord package in python to do ordinal logit regression (predict response to movie rating 1-5 stars).
One of my predictor variables is also ordinal but ...
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Selecting features for malware analysis
I am trying to build a classifier that detects if I have a malaware by predicting the provenance compiler. To do so I have a dataset composed of assembly code in json format :
In particular, I want ...
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SVM, which range to use when normalizing
I am using the SVM classifier from Scikit Learn.
I was wondering is there is a know-best-practice when it comes to normalization. I'm using different normalization tecniques, but all my normalized ...
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PCA scikit-learn - ValueError: array must not contain infs or NaNs
I use PCA from from sklearn.decomposition to reduce data dimension.
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Sklearn LocalOutlierFactor contamination parameter
Can anyone provide an intuitive explanation of the choice of contamination parameter used in sklearn's LocalOutlierFactor implementation when ...
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Majority voting in scikit-learn Random forest
My main concern is that i need to understand that how does the random forest do majority voting in scikit learn source code. I did not find that specific code in source code of RandomForest. if ...
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TS-SS and Cosine similarity among text documents using TF-IDF in Python
A common way of calculating the cosine similarity between text based documents is to calculate tf-idf and then calculating the linear kernel of the tf-idf matrix.
TF-IDF matrix is calculated using ...
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Temporal outlier Analysis on sensor data
I am working to find anomaly/outliers in sensor data using unsupervised machine learning (without training dataset). I have around 20000 samples taken per minute of various sensors. I just need to ...
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What to do with large number of collinear variables?
I have this time-series dataset that has 63 features, out of which 57 were manually engineered. While checking for collinearity, I get this correlation matrix:
As can be seen there are a number of ...
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Are pipelines capable of cacheing intermediate results?
I use pandas to do feature extraction for machine learning.
I hope to achieve the following: Consider I have five data processing steps done sequentially, and if I execute them once, the results will ...
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Explaining feature_importances_ in Scikit Learn RandomForestRegressor
For a project, I used the feature_importances_ attributes from the RandomForestRegressor. Everything works well but I don't know ...
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LightGBM vs Sklearn LightGBM- Mistake in Implementation- Exact same parameters giving different results
While passing the exact same parameters to LightGBM and sklearn's implementation of LightGBM, I am getting different results. Initially, I was getting the exact same results on doing this, however, I ...
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Cross-fold validation done on whole dataset or training set?
I have a dataset of 77 samples with 302 features with two labels (0,1). I trained an SVM with gridsearch (cv=5) to perform binary classification.
In one run of my script, I do a test-train split, ...
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Value Error: MSLE & CrossVal
I'm trying to run cross validation with mean squared log error with sklearn and getting the following error message:
...
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What is the use of fit method in sklearn.preprocessing.Normalizer()?
According to the documentation of fit(self, X[, y]) method of sklearn.preprocessing.Normalizer(), it does nothing and return the estimator unchanged.
I understand that if I intend to normalize data I ...
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How to Split And Resample Imbalanced Dataset Into Train, Validation and Test
I want to understand how to split the imbalanced data set with a binary target variable where 87% of the samples are negative and 13% of the samples are positive. Now, I know that you should always ...
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Gradient decent in Python
I just finished working on my first machine learning algorithm i.e Linear regression. I want to reduce the rmse by optimising the model. I found out that gradient decent does the same job. But i dont ...
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class_weight on sklearn's DecisionTreeClassifier
Can class_weight='balanced' on scikit-learn's DecisionTreeClassifier be interpreted as having identical duplicate data points for the minority classes?
I know that doesn't work that way, class_weight ...
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Hierarchical Clustering: Extract observations from large heatmap
I'm currently trying to visualize a large data set as heat map. That in itself works smoothly but I struggle with gaining insights from interestingly looking clusters.
Specifically, I have two ...
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