Questions tagged [lasso]

Least Absolute Shrinkage and Selection Operator (LASSO) regression, is a regularization technique used in regression cases where the model overfits or there is high multi-collinearity.

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The ideal function in R for fit fitting n LASSO Regressions on n data sets

As part of a statistical learning research paper I am collaborating on, I am running/fitting two hundred sixty thousand different LASSO Regressions on the same number of different randomly generated ...
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Having problems replicating the results of a series of N LASSOs fit to N datasets in R

I have fit n LASSO Regressions on n different data sets (the 'datasets' object is an R list of length n where each element is a data.table which is a light and fast data frame from the data.table ...
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How to multiply the boolean/binary values in 1 row in a dataframe by the chr values in another row with the product landing in the original row

Suppose that I already know which subset out of a set of 30 candidate regressor columns are the true regressors included in the structural equation describing that dataset (because I do by ...
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A good 4th Benchmark method to compare the performance of a novel Variable Selection Algorithm being evaluated

I am collaborating on a research project with a respected econometrician as a graduate student (although only in an MS program, not PhD program mind you) exploring the properties and comparing the ...
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Which standardization technique to use for Lasso regression?

I am fitting a Lasso Regression to do feature selection in my dataset. I have seen it is common practice to use StandardScaler to standardize the dataset. However, given that the distribution of my ...
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L2 and L1 regularization: Why avoiding mutual cancelations of errors cause the skewness (bias) can't be determined?

I'm trying to understand about the L1 and L2 regularization and in this paper (https://arxiv.org/pdf/1809.03006.pdf) the authors mentioned the problem with using the mean absolute error or mean ...
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Efficient computation for ridge regression

In the book "Introduction to Statistical learning with R" https://hastie.su.domains/ISLR2/ISLRv2_website.pdf , authors say on page 247 - "There are very efficient algorithms for fitting ...
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A simple way to store the factors selected by (BE) Stepwise Regression n run on N datasets via lapply, a For Loop then an lapply, or a function?

I am currently doing research with a coauthor and collaborator comparing a new optimal model selection procedure he has proposed via Monte Carlo Simulation of the new procedure vs 2 benchmarks, LASSO &...
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Group lasso and feature selection

I have a dataset with a lot of categorical variables and a binary target variable and I want to put it to an svm. I converted the categorical variables to dummy variables and since my observations are ...
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LassoCV model training with 150+ features

I'm trying to fit a dataset of about 150 features using LassoCV. Right now I'm using this loop to try and find the best value for alpha (get_tts is just a function to retrieve test/train sets): ...
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Lasso with cross validation - is training/test set split nedeed?

I am using LASSO regression to identify the most important variables for predicting an outcome. I have a relatively small sample (~1000 observations), and I was planning to use cross-validation to ...
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Regularizing the intercept - particular case

Yesterday I posted this thread Regularizing the intercept where I had a question about penalizing the intercept. In short, I asked wether there exist cases where penalizing the intercept leads to a ...
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How to compare between two methods of multivariate to filling NA

In the Titanic dataset, I performed two methods to fill Age NA. The first one is regression using Lasso: ...
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How to extract MSEP or RMSEP from lassoCV?

I'm doing lasso and ridge regression in R with the package chemometrics. With ridgeCV it is easy to extract the SEP and MSEP values by ...
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Why is the L2 penalty squared but the L1 penalty isn't in elastic-net regression?

There was some data set I worked with which I wanted to solve non negative least squares (NNLS) on and I wanted a sparse model. After a bit of experiementing I found that what worked the best for me ...
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Why is gridsearchCV.best_estimator_.score giving me r2_score even if I mentioned MAE as my main scoring metric?

I have a lasso regression model with the following definition : ...
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Is it possible to explain why Lasso models eliminated certain coefficient?

Is it possible to understand why Lasso models eliminated specific coefficients?. During the modelling, many of the highly correlated features in data is being eliminated by Lasso regression. Is it ...
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Accessing regression coefficients when using MultiOutputRegressor

I am working on a multioutput (nr. targets: 2) regression task. The original data has a huge dimensionality (p>>n, i.e. there are far more predictors than ...
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Predicting single floats based on set of 2 feature arrays each of 100 values

I am trying to predict audio to video desynchronization based on set of two arrays of lenght 100 which consist of coresponding audio and video samples. The problem is that my labels are single floats (...
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Why are we not checking the significance of the coefficients in Lasso and elastic net models

As far as I know, we don't check the coefficient significance in Lasso and elasticnet models. Is it because insignificant feature coefficients will be driven to zero in these models?. Does that mean ...
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Elegant way to plot the L2 regularization path of logistic regression in python?

Trying to plot the L2 regularization path of logistic regression with the following code (an example of regularization path can be found in page 65 of the ML textbook Elements of Statistical Learning ...
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Do I have to remove features with pairwise correlation even if I am doing a regularized logistic regression?

Normally we would remove features that have high pairwise correlation with another feature before performing regression. But is this step necessary if I am applying L2 regularized logistic regression (...
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Can I rescale TF matrix or TF-IDF matrix using StandardScaler prior to Logisitc Lasso regression?

I am trying to use Logistic Lasso to classify documents as 1 or 0. I've tried using both the TF matrix and TF-IDF matrix representations of the documents as my predictors. I've found that if I use the ...
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What's the correct cost function for Linear Regression

As we all know the cost function for linear regression is: Where as when we use Ridge Regression we simply add lambda*slope**2 but there I always seee the below as cost function of linear Regression ...
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How to set coefficient limit in lasso regression in Python?

I'm working on a regression problem where I want to use Lasso model. With the help of Lasso and LassoCV, we can provide different alpha values and get the best parameter and coefficients however I ...
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Difference between PCA and regularisation

Currently, I am confusing about PCA and regularisation. I wonder what is the difference between PCA and regularisation: particularly lasso (L1) regression? Seems both of them can do the feature ...
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Lack of standardization in Kaggle jupyter notebooks when using lasso/ridge?

I've recently started using Kaggle, and I've noticed that for a lot of these jupyter notebooks written by others, when they use Ridge/Lasso, they don't standardize the non-categorical numerical ...
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sklearn gridsearch lasso regression: find specific number of coefficients

I am using GridSearchCV and Lasso regression in order to fit a dataset composed out of Gaussians. I keep this example similar to this tutorial. My goal is to find the best solution with a restricted ...
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Lasso regression not getting better without random features

First of all, I'm new to lasso regression, so sorry if this feels stupid. I'm trying to build a regression model and wanted to use lasso regression for feature selection as I have quite a few features ...
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How to remove features from a sklearn pipeline after it has already been fitted?

Background: I have created a basic modeling workflow in sklearn that utilizes sklearn's pipeline object. There are some preprocessing steps within the pipeline, and the last step of the pipeline is to ...
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1 answer
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Lasso Regression for Feature Importance saying almost every feature is unimportant?

I have a metric (RevenueSoFar) that is a great predictor of my target FinalRevenue as you'd expect - it is a metric where we tend to get 90-95% of revenue so far on day 1 and then it can increase over ...
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Interpreting machine learning coefficients

My dog show predictive tool is having some trouble with its neural net. Broadly, I start with a couple of factors--age, weight, height, breed (which is a set of dummy variables), a subjective cuteness ...
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What is the meaning of the sparsity parameter

Sparse methods such as LASSO contain a parameter $\lambda$ which is associated with the minimization of the $l_1$ norm. Higher the value of $\lambda$ ($>0$) means that more coefficients will be ...
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how Lasso regression helps to shrinks the coefficient to zero and why ridge regression dose not shrink the coefficient to zero?

How does Lasso regression help with feature selection of model by making the coefficient shrink to zero? I could see few below with below diagram. Can any please explain in simple terms how to ...
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regarding lasso.score in lasso modeling using scikit-learn

I once saw the following code segment of using lasso model based on scikit-learn ...
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Generating artificial data to extend learning set

I have dataset containing 42 instances(X) and one final Y on which i want to perform LASSO regression.All are continuous and numerical. As the sample size small, I wish to extend it. I am kind of ...
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How do standardization and normalization impact the coefficients of linear models?

One benefit of creating a linear model is that you can look at the coefficients the model learns and interpret them. For example, you can see which features have the most predictive power and which do ...
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How is learning rate calculated in sklearn Lasso regression?

I was applying different regression models to Kaggle Housing dataset for advanced regression. I am planning to test out lasso, ridge and elastic net. However, none of these models have learning rate ...
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Lasso stricter with more data

I am currently analyzing investment strategies, and have implemented a backtest accordingly. This essentially means that I predict returns each month by using all prior historical data. Consequently, ...
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For a square matrix of data, I achieve $R^2=1$ for Linear Regression and $R^2=0$ for Lasso. What's the intuition behind?

For a square matrix of random data, N columns and N rows. I am fitting two models, linear regression and Lasso. For the linear regression, I achieve a perfect score in train set, while in the Lasso I ...
2 votes
1 answer
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When should we start using stacking of models?

I am solving a Kaggle contest and my single model has reached score of 0.121, I'd like to know when to start using ensembling/stacking to improve the score. I used lasso and xgboost and there ...
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Normalisation results in R^2 score of 0 - Lasso regression

I am running a regression analysis on a 7000 row dataset with a train/test split of 70%/30%. I am using one variable X to predict a variable ...
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Scikit-compatible network lasso implementation

Is anyone aware of a scikit-compatible network Lasso (nLasso) implementation? These papers have source code as well: D. Hallac, J. Leskovec, and S. Boyd, “Network lasso: Clustering and ...
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LASSO remaining features for different penalisation

I am using the sklearn LASSOCV function and I am changing the penalisation parameter in order to adjust the number of features killed off. For example for $\alpha = 0.01$ I have 55 features remaining ...
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Why does Lasso behave "erratically" when the number of features is greater than the number of training instances?

From the book "Hands-on Machine Learning with Scikit-Learn and TensorFlow 2nd edition" chapter 4: In general, Elastic Net is preferred over Lasso since Lasso may behave erratically when the ...
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Why do you need to use group lasso with categorical variables?

From what I've read you should you use group lasso to either discard the dummy encoded variables (of the category) or use all of them. If you use normal lasso then some of the variables in the group ...
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What happens when scikit-learn does a Lasso Model?

I have started an MLS course. As a beginner and non-mathematician it has been hard. I am trying to understand the exercise about Lasso Models. I have done Lasso models on R-cran, but this is my first ...
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Lasso implementation Drawback

Recently I've been trying to implement Lasso by myself in R, not using the "glmnet" package, and based on an article by Tibshirani I wrote a raw code to implement coordinate descent method, and it ...