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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What's the difference between fit and fit_transform in scikit-learn models?
I do not understand the difference between the fit and fit_transform methods in scikit-learn. Can anybody explain simply why we ...
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Train/Test/Validation Set Splitting in Sklearn
How could I randomly split a data matrix and the corresponding label vector into a X_train, X_test, ...
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When to use One Hot Encoding vs LabelEncoder vs DictVectorizor?
I have been building models with categorical data for a while now and when in this situation I basically default to using scikit-learn's LabelEncoder function to transform this data prior to building ...
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SVM using scikit learn runs endlessly and never completes execution
I am trying to run SVR using scikit-learn (python) on a training dataset that has 595605 rows and 5 columns (features) while the test dataset has 397070 rows. The data has been pre-processed and ...
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ValueError: Input contains NaN, infinity or a value too large for dtype('float32')
I got ValueError when predicting test data using a RandomForest model.
My code:
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6
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strings as features in decision tree/random forest
I am doing some problems on an application of decision tree/random forest. I am trying to fit a problem which has numbers as well as strings (such as country name) as features. Now the library, scikit-...
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Does scikit-learn have a forward selection/stepwise regression algorithm?
I am working on a problem with too many features and training my models takes way too long. I implemented a forward selection algorithm to choose features.
However, I was wondering does scikit-learn ...
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Difference between OrdinalEncoder and LabelEncoder
I was going through the official documentation of scikit-learn learn after going through a book on ML and came across the following thing:
In the Documentation it is given about ...
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train_test_split() error: Found input variables with inconsistent numbers of samples
Fairly new to Python but building out my first RF model based on some classification data. I've converted all of the labels into int64 numerical data and loaded into X and Y as a numpy array, but I am ...
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Understanding predict_proba from MultiOutputClassifier
I'm following this example on the scikit-learn website to perform a multioutput classification with a Random Forest model.
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StandardScaler before or after splitting data - which is better?
When I was reading about using StandardScaler, most of the recommendations were saying that you should use StandardScaler before ...
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How to force weights to be non-negative in Linear regression
I am using a standard linear regression using scikit-learn in python.
However, I would like to force the weights to be all non-negative for every feature. is there any way I can accomplish that? I was ...
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Calculating KL Divergence in Python
I am rather new to this and can't say I have a complete understanding of the theoretical concepts behind this. I am trying to calculate the KL Divergence between several lists of points in Python. I ...
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Sentence similarity prediction
I'm looking to solve the following problem: I have a set of sentences as my dataset, and I want to be able to type a new sentence, and find the sentence that the new one is the most similar to in the ...
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Why is xgboost so much faster than sklearn GradientBoostingClassifier?
I'm trying to train a gradient boosting model over 50k examples with 100 numeric features. XGBClassifier handles 500 trees within 43 seconds on my machine, while <...
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How can I check the correlation between features and target variable?
I am trying to build a Regression model and I am looking for a way to check whether there's any correlation between features and target variables?
This is my ...
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What is the reason behind taking log transformation of few continuous variables?
I have been doing a classification problem and I have read many people's code and tutorials. One thing I've noticed is that many people take np.log or ...
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How to get p-value and confident interval in LogisticRegression with sklearn?
I am building a multinomial logistic regression with sklearn (LogisticRegression). But after it finishes, how can I get a p-value and confident interval of my model? It only appears that sklearn only ...
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How to calculate the fold number (k-fold) in cross validation?
I am confused about how I choose the number of folds (in k-fold CV) when I apply cross validation to check the model. Is it dependent on data size or other parameters?
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Improve the speed of t-sne implementation in python for huge data
I would like to do dimensionality reduction on nearly 1 million vectors each with 200 dimensions(doc2vec).
I am using TSNE ...
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Scikit-learn: Getting SGDClassifier to predict as well as a Logistic Regression
A way to train a Logistic Regression is by using stochastic gradient descent, which scikit-learn offers an interface to.
What I would like to do is take a scikit-learn's SGDClassifier and have it ...
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How to deal with string labels in multi-class classification with keras?
I am newbie on machine learning and keras and now working a multi-class image classification problem using keras. The input is tagged image. After some pre-processing, the training data is represented ...
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RandomForestClassifier OOB scoring method
Does the random forest implementation in scikit-learn use mean accuracy as its scoring method to estimate generalization error with out-of-bag samples? This is not mentioned in the documentation, but ...
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What is the difference between cross_validate and cross_val_score?
I understand cross_validate and how it works, but now I am confused about what cross_val_score actually does. Can anyone give ...
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Can you explain the difference between SVC and LinearSVC in scikit-learn?
I've recently started learning to work with sklearn and have just come across this peculiar result.
I used the digits dataset ...
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1
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What's the difference between Sklearn F1 score 'micro' and 'weighted' for a multi class classification problem?
I have a multi-class classification problem with class imbalance. I searched for the best metric to evaluate my model. Scikit-learn has multiple ways of calculating the F1 score. I would like to ...
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When to use Standard Scaler and when Normalizer?
I understand what Standard Scalar does and what Normalizer does, per the scikit documentation: Normalizer, Standard Scaler.
I know when Standard Scaler is applied. But in which scenario is Normalizer ...
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21
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3
answers
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Pandas Dataframe to DMatrix
I am trying to run xgboost in scikit learn. And I am only using Pandas to load the data into a dataframe. How am I supposed to use pandas df with xgboost? I am confused by the DMatrix routine ...
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How to adjust the hyperparameters of MLP classifier to get more perfect performance
I am just getting touch with Multi-layer Perceptron. And, I got this accuracy when classifying the DEAP data with MLP. However, I have no idea how to adjust the hyperparameters for improving the ...
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How to perform one hot encoding on multiple categorical columns
I am trying to perform one-hot encoding on some categorical columns. From the tutorial I am following, I am supposed to do LabelEncoding before One hot encoding. I have successfully performed the ...
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What is the difference between CountVectorizer token counts and TfidfTransformer with use_idf set to False?
We can use CountVectorizer to count the number of times a word occurs in a corpus:
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What is the difference between a hashing vectorizer and a tfidf vectorizer
I'm converting a corpus of text documents into word vectors for each document. I've tried this using a TfidfVectorizer and a HashingVectorizer
I understand that a ...
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4
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Incremental Learning with sklearn: warm_start, partial_fit(), fit()
I have built an ML model with the goal of making predictions for targets of the following week. In general, new data will come in and be processed at the end of each week and be in the same data ...
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How does SelectKBest work?
I am looking at this tutorial: https://www.dataquest.io/mission/75/improving-your-submission
At section 8, finding the best features, it shows the following code.
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scikit-learn n_jobs parameter on CPU usage & memory
In most estimators on scikit-learn, there is an n_jobs parameter in fit/predict methods for ...
user29151
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Merging sparse and dense data in machine learning to improve the performance
I have sparse features which are predictive, also I have some dense features which are also predictive. I need to combine these features together to improve the overall performance of the classifier.
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1
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Algorithms for text clustering
I have a problem of clustering huge amount of sentences into groups by their meanings. This is similar to a problem when you have lots of sentences and want to group them by their meanings.
What ...
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Where in the workflow should we deal with missing data?
I'm building a workflow for creating machine learning models (in my case, using Python's pandas and sklearn packages) from data ...
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How to use Scikit-Learn Label Propagation on graph structured data?
As part of my research, I am interested in performing label propagation on a graph. I am especially interested in those two methods:
Xiaojin Zhu and Zoubin Ghahramani. Learning from labeled and ...
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Does scikit-learn use regularization by default?
I just fitted a logistic curve to some fake data. I made the data essentially a step function.
data = -------------++++++++++++++
But when I look at the fitted ...
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How to use SimpleImputer Class to replace missing values with mean values using Python?
This is my code
...
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Predict the best time of call
I have a dataset including a set of customers in different cities of California, time of calling for each customer, and the status of call (True if customer answers the call and False if customer does ...
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How can I fit categorical data types for random forest classification?
I need to find the accuracy of a training dataset by applying Random Forest Algorithm. But my the type of my data set are both categorical and numeric. When I tried to fit those data, I get an error.
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Using TF-IDF with other features in scikit-learn
What is the best/correct way to combine text analysis with other features? For example, I have a dataset with some text but also other features/categories. scikit-learn's TF-IDF vectorizer transforms ...
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Efficient dimensionality reduction for large dataset
I have a dataset with ~1M rows and ~500K sparse features. I want to reduce the dimensionality to somewhere in the order of 1K-5K dense features.
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How to train model to predict events 30 minutes prior, from multi-dimensionnal timeseries
Experts in my field are capable of predicting the likelyhood an event (binary spike in yellow) 30 minutes before it occurs. Frequency here is 1 sec, this view represents a few hours worth of data, i ...
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How to do stepwise regression using sklearn? [duplicate]
I could not find a way to stepwise regression in scikit learn. I have checked all other posts on Stack Exchange on this topic. Answers to all of them suggests using f_regression.
But f_regression ...
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How max_features parameter works in DecisionTreeClassifier?
What is the parameter max_features in DecisionTreeClassifier responsible for?
I thought it defines the number of features the ...
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Feature selection using feature importances in random forests with scikit-learn
I have plotted the feature importances in random forests with scikit-learn. In order to improve the prediction using random forests, how can I use the plot information to remove features? I.e. how to ...
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Irregular Precision-Recall Curve
I'd expect that for a precision-recall curve, precision decreases while recall increases monotonically. I have a plot that is not smooth and looks funny. I used scikit learn the values for plotting ...
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