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.

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Need new strategy for single class classifier

I am attempting to create a single class classifier where the classes are fairly close to balanced (+/- 25). My dataset has ~2,800 samples and ~1,100 features. All of the features are binary except ...
M Arroyo's user avatar
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1 answer
3k views

Learning curves - Why does the training accuracy start so high, then suddenly drop?

I implemented a model in which I use Logistic Regression as classifier and I wanted to plot the learning curves for both training and test sets to decide what to do next in order to improve my model. ...
DiamondDogs95's user avatar
23 votes
2 answers
23k views

What is the difference between $R^2$ and variance score in Scikit-learn?

I was reading about regression metrics in the python scikit-learn manual and even though each one of them has its own formula, I cannot tell intuitively what is the difference between $R^2$ and ...
hipoglucido's user avatar
0 votes
1 answer
497 views

Using centroids to find predictive cluster features

I clustered some data (rows: text documents, columns: word frequencies) using the KMeans implementation in Scikit Learn. This, like most other centroid-based clustering algorithms, returns a centroid ...
patrick's user avatar
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2 answers
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Random Forest model good train and test performance but bad "real world" performance

I am working on a classification problem where I need to classify objects based on a visual data. There are a couple hundred different classifications to be made and I have around a million plus ...
Matt Camp's user avatar
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1 answer
4k views

Ensemble models perform worse than single one

In my model testing, I tried to use model ensembling (blending in this case) to get better results. However the ensemble cannot beat single RandomForrestClassifier. In first layer, I train meta-...
HonzaB's user avatar
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1 answer
92 views

Machine learning step order question

I have been working on this project for over a year now and I believe i finally have things figured out. Mainly i'm looking for any suggestions or things i'm doing wrong with my process, but i also ...
user79587's user avatar
1 vote
0 answers
394 views

Generate code for sklearn's GradientBoostingClassifier

I want to generate code (Python for now, but ultimately C) from a trained gradient boosted classifier (from sklearn). As far as I understand it, the model takes an initial predictor, and then adds ...
Pokey McPokerson's user avatar
2 votes
1 answer
81 views

In a CART model, why is the average of the leaf proportions equal to the total proportion only when the classes are unweighted?

Suppose I want to do binary classification (the two classes are 0 and 1) and I choose to work with a CART model. I first fit this model on a training set. (Note that I am using Python, and ...
augustin-barillec's user avatar
3 votes
1 answer
2k views

Do you need to scale Vectorizers in sklearn?

I have a set of custom features and a set of features created with Vectorizers, in this case TfidfVectorizer. All of my custom features are simple np.arrays (e.g. [0, 5, 4, 22, 1]). I am using ...
Ivan Bilan's user avatar
2 votes
0 answers
867 views

Why is my SVM multiclass classifier only correctly predicting a few classes?

I'm doing an online course to learn the basics of Machine Learning. This exercise is on how to use a SVM classifier with multiple classes. While the problem is specific to question 2 from this ...
blue_chip's user avatar
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Linear SVM feature weights interpretation. Binary classification, only positive feature values

I'm using clf = svm.SVC(kernel='linear') on a data set with only two classes $y \in \{-1, +1\}$ and the feature values of all samples are normalized between 0 and 1....
kerryz's user avatar
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15 votes
1 answer
9k views

Difference between ElasticNet in scikit-learn Python and Glmnet in R

Has anybody tried to verify whether fitting an Elastic Net model with ElasticNet in scikit-learn in Python and glmnet in R on ...
Dion's user avatar
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2 votes
0 answers
1k views

Strategy for finding optimal bagging parameters

I am using a BaggingClassifier of SVMs in sklearn. What is the best strategy for finding optimal parameters, using my training/vaildation data? When using the full dataset, I can use grid search to ...
Chris Parry's user avatar
1 vote
1 answer
2k views

Assigning weights to a multilabel SVM to balance classes

How is this done? I am using Sklearn to train an SVM. My classes are unbalanced. Note that my problem is multiclass, multilabel so I am using OneVsRestClassifier: <...
Chris Parry's user avatar
1 vote
0 answers
45 views

Modelling house energy production using month as a variable

I'm attempting to model the energy production of a set of houses for which data on temperature and daylight over 22 months is available. The data is arranged such as such: ...
KRS-fun's user avatar
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1 answer
2k views

Find max value of random forest regressor output

I was wondering, for scikit learns regressors (extra trees, random forest regressor etc), how can i find the combination of inputs that would give me the max value of the target variable? Other than ...
Waffleboy's user avatar
8 votes
1 answer
2k views

Difference in partial dependence calculated by R and Python

I noticed there's a difference in partial dependence calculated by R package gbm and Python's scikit-learn. Here's ...
abudis's user avatar
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1 answer
298 views

random forest for density estimation using sklearn [closed]

I want to use or extend sklearn-randomForest for density estimation. I don't know to tackle it. I read A. Criminisi and his team work on random forest as a unified framework where they first ...
ISMAEL KONE's user avatar
2 votes
2 answers
1k views

Is it legitimate to modify the classification of an scikit-learn random forest classifier by changing its default threshold?

I am using a random forest binary classifier (in sklearn) in Python to detect anomalous events with an extremely unbalanced class dataset (1% are positive and 99% are negative). My recall score for ...
user3824382's user avatar
23 votes
2 answers
34k views

Difference between selecting features based on "F regression" and based on $R^2$ values?

Is comparing features using F-regression the same as correlating features with the label individually and observing the $R^2$ value? I have often seen my ...
makansij's user avatar
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17 votes
3 answers
8k views

Logistic Regression: Scikit Learn vs glmnet

I am trying to duplicate the results from sklearn logistic regression library using glmnet package in R. From the ...
hurrikale's user avatar
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1 vote
0 answers
88 views

People detection in a supermarket zenital video

I am programming a people detector in a supermarket environment. I am using a dataset that I built from an example vídeo with 500 people and 500 background pictures which have 128x128 size with HOG ...
Pablo Alcala's user avatar
59 votes
3 answers
123k views

Logistic Regression: Scikit Learn vs Statsmodels

I am trying to understand why the output from logistic regression of these two libraries gives different results. I am using the dataset from UCLA idre tutorial, predicting ...
hurrikale's user avatar
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1 vote
1 answer
2k views

What do eps and tol do in LassoCV (scikit-learn)

Using scikit-learn: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoCV.html Specifically, I am interested in: 1) If eps grows, does the accuracy(precision) increase or ...
The Baron's user avatar
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1 answer
41 views

Designing a training set for regression on probabiltiy values given time , categorical and continous features

Assume we have following variables out of which "Probability of sale " needs to be predicted , and this is to be done for a portable business vendor whose location changes with time : ...
stats101's user avatar
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0 answers
883 views

Image Segmentation with a challenging background

[cross-posted from datascience, as no answers received] I'm working on an animal classification problem, with the data extracted from a video feed. The recording was made in a pen, so the problem is ...
Alex's user avatar
  • 280
0 votes
1 answer
4k views

How to combine the results of several clustering with scikit-learn?

I am trying to fit several cluster algorithms on one or across several subsets of a data matrix X, of shape (n_samples, n_features). For example: ...
kingjr's user avatar
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4 votes
1 answer
3k views

Is it necessary to use warm_start when tracking oob_score in scikit RandomForestClassifier?

I'm planning on doing feature-selection with RandomForestClassifier by using the feature_importances and ...
ihadanny's user avatar
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1 vote
1 answer
644 views

Different predictions on multiple run of the same algorithm scikit neural network

Since a MLP can implement any function. I have the following code, using which I am trying to implement the AND function. But what I find that on running the program multiple times, I end up getting ...
Achyuta Aich's user avatar
2 votes
1 answer
2k views

How to reduce dimensionality of audio data that comes in form of matrices and vectors?

I'm working on a project involved with identifying different types of sounds (such as screams, singing, and bangs) from each other. We've got our data a reasonable number of different transformations ...
Ben Sandeen's user avatar
2 votes
2 answers
3k views

Lasso with constraint on some coefficients (not all)

I would like to run a lasso regression (L1 penalisation) with a twist: there are different constraints on my problem. The coefficients for my features (predictors) are $\beta_i$. I want to find the $...
DevShark's user avatar
  • 171
0 votes
1 answer
1k views

Minimizing residual sum of squares formula

I recently saw a question on the scikit-learn mailing list that I had wondered about. This is the formula to minimize the residual sum of squares. http://scikit-learn.org/stable/modules/linear_model....
user2434291's user avatar
5 votes
2 answers
2k views

Predictive modeling with feature selection using a small sample size?

I am trying to build a predictive model for a binary classification problem. I have 200,000 features and 100 samples. I want to reduce the # of features and not over-fit the model, all while being ...
user1566200's user avatar
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1 vote
1 answer
736 views

Recommender System + Collaborative filtering without users

So I have a problem where I have a dataset that includes a list of Tools that are tied to Tasks. The data is structured as follows: The users do not rate the Tools, they simply use them in a method ...
frei's user avatar
  • 111
8 votes
1 answer
11k views

Sampling from Gaussian Process Posterior

Anyone know of a Python package that both fits a Gaussian Process to data, and also lets you sample paths from the posterior? I'm interested in sampling the colorful lines on right (b) of the ...
maurice's user avatar
  • 273
3 votes
0 answers
71 views

What is the best way to simultaneously fit multiple binomial and continuous predictors?

What is the most efficient way to fit a linear model w so that Y = w . X, where X is a ...
kingjr's user avatar
  • 31
0 votes
1 answer
133 views

How this code of cross-validation work? [closed]

I am new in sklearn and I try to learn how to use cross-validation to choose the best model of an SVM. I found this example How to split the dataset for cross validation, learning curve, and final ...
BetterEnglish's user avatar
5 votes
1 answer
5k views

Is it possible to share models between R, scikit-learn and spark?

If I create machine learning models in Python or R, is it possible to export the models in a format that could be imported by spark MLlib?
Chris Snow's user avatar
1 vote
0 answers
147 views

features selection - methods based on estimated feature importances vs. methods based on scores

I noticed that all feature selection methods implemented in sklearn are based on external estimator that assigns weights to features, AKA feature_importances. I ...
ihadanny's user avatar
  • 3,280
4 votes
1 answer
6k views

Random forest low score on testing data (scikit-learn)

I am trying to train my model using Scikit-learn's Random forest (Regression) and have tried to use GridSearch with Cross-validation (CV=5) to tune hyperparameters. I fixed ...
Muhammad's user avatar
3 votes
1 answer
4k views

Logistic regression using ANOVA kernel in SKLearn?

In RapidMiner, you can run a logistic regression classifier with multiple kernel types. I see no options in sklearn.linear_models.LogisticRegression. Does anybody ...
user1566200's user avatar
  • 1,047
3 votes
2 answers
721 views

Features, samples, and over-fitting?

I have a data set with 30 samples, 2 classes, and 100,000 features. When I run an SVC classifier on it from SKLearn using stratified cross-validation, the accuracy is barely better than chance. After ...
user1566200's user avatar
  • 1,047
1 vote
0 answers
2k views

How would one use KDE as a one 1D clustering method?

I need to cluster a simple univariate data set into a preset number of clusters. Technically it would closer to binning or sorting the data since it is only 1D, but my boss is calling it clustering, ...
Skander H.'s user avatar
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2 votes
2 answers
8k views

Weighting time series data for prediction

I am building a simple random forest to predict soccer results in sckit. I simply train the model to predict each teams score based on various features. However I am trying to think how I can weight ...
Marcus's user avatar
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8 votes
0 answers
4k views

Which standard deviation of the cross-validation score?

When doing cross-validation for model selection, I found there are many ways to quote the "standard deviation" for the cross-validation scores (here "score" means an evaluation metric e.g. accuracy, ...
xiaoxiao87's user avatar
4 votes
2 answers
939 views

Question about the Scikit-learn "SVM-Anova: SVM with univariate feature selection" example

Can anyone explain to me why in the Scikit-learn "SVM-Anova: SVM with univariate feature selection" example http://scikit-learn.org/stable/auto_examples/svm/plot_svm_anova.html when we use all ...
xiaoxiao87's user avatar
3 votes
0 answers
99 views

Car out of route

I have one KML file that describes the movement of a car. Data comes from sensor in the car, contains wrong or noisy measurements. I want to filter the wrong measurements, i.e. throw away the ...
Duygu's user avatar
  • 41
3 votes
2 answers
7k views

Ensembling with VotingClassifier

I am using VotingClassifier from sklearn.ensemble however i am puzzled with the results. Consider following algorithms: ...
HonzaB's user avatar
  • 683
6 votes
1 answer
7k views

Cross-validation vs random sampling for classification test

I usually have used cross-validation for testing classification performance. However, I read about the article that random sampling (bootstrapping) works better in many cases. I am not sure which one ...
ToBeSpecific's user avatar

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