# Questions tagged [regression]

Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

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23k views

### Why do we need to discard one dummy variable?

I have learned that, for creating a regression model, we have to take care of categorical variables by converting them into dummy variables. As an example, if, in our data set, there is a variable ...
355 views

### Confidence interval interpretation in linear regression when errors are not normally distributed

I've read that "If the error distribution is significantly non-normal, confidence intervals may be too wide or too narrow" (source). So, can anyone elaborate on this? When are the confidence intervals ...
482 views

### Removing constant from the regression model

I am trying to calibrate two variables $(X,Y)$ of different measuring techniques from two instruments, the result of the linear regression analysis appears as shown in the image. The result shows the ...
3k views

### Solving a system of equations with sparse data

I am attempting to solve a set of equations which has 40 independent variables (x1, ..., x40) and one dependent variable (y). The total number of equations (number of rows) is ~300, and I want to ...
2k views

### Can Boosted Trees predict below the minimum value of the training label?

I am using gradient Gradient Boosted Trees (with Catboost) for a Regression task. Can GBtrees predict a label that is below the minimum (or above the max) that was seen in the training ? For instance ...
2k views

### xgboost: Is there a way to perform regression on rates/percentages data?

I have a dependent variable, $Y$, that is made up of rates/percentages data, so each value is between $0$ and $1$. I was attracted to the xgboost library because it allows focusing in on specific ...
561 views

### What is the difference between classification and regression?

I understand classification....a discrete response or category, like animal is dog or cat. The author says..."Regression techniques predict continuous changes such as the change in temperature, power ...
71k views

### Validation loss is not decreasing

I am trying to train a LSTM model. Is this model suffering from overfitting? Here is train and validation loss graph:
10k views

### MAD vs RMSE vs MAE vs MSLE vs R²: When to use which?

In regression problems, you can use various different metrics to check how well your model is doing: Mean Absolute Deviation (MAD): In $[0, \infty)$, the smaller the better Root Mean Squared Error (...
19k views

### Get multiple output from Keras

I have a regression problem which I have to predict 3 numerical values from a provided data. For example let's say I have a data set containing ...
7k views

### GridSearch without CV

I create a Random Forest and Gradient Boosting Regressor by using GridSearchCV. For the Gradient Boosting Regressor, it takes too long for me. But I need to know which are the best parameters for the ...
9k views

### Neural network for Multiple integer output

I have a data set that contains 135 input features and 132 output values to be predicted. The input features are all numeric floating point values and each output value would be an integer between [0,...
111 views

### Regression Algorithms in Production

I am interested in predicting if a doctor would prescribe a specific drug and have chosen Logistic Regression as a starting point. I have a few questions: Is feature selection the first step to take ...
28 views

### How to analyze repeated measure data for prediction?

In my work, we collect sales data of our products. We have a set of 1st level customers (lets call that group as jacks) with whom we do we business. These jacks then sell our products to end customers ...
111 views

### ANN regression accuracy and loss stuck

I have a data set on predicting solar power generation, the dataset has 20 independent var and 1 dependent. The accuracy of my model is stuck at 60%. I have tried several models but this accuracy is ...
48k views

### Neural Network for Multiple Output Regression

I have a dataset containing 34 input columns and 8 output columns. One way to solve the problem is to take the 34 inputs and build individual regression model for each output column. I am wondering ...
40k views

### What does "linear in parameters" mean?

The model of linear regression is linear in parameters. What does this actually mean?
18k views

### Why do we convert skewed data into a normal distribution

I was going through a solution of the Housing prices competition on Kaggle (Human Analog's Kernel on House Prices: Advance Regression Techniques) and came across this part: ...
5k views

### Is there a library that would perform segmented linear regression in python?

There is a package named segmented in R. Is there a similar package in python?
33k views

### Xgboost - How to use feature_importances_ with XGBRegressor()?

How could we get feature_importances when we are performing regression with XGBRegressor()? There is something like ...
14k views

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9k views

### Neural network with flexible number of inputs?

Is it possible to create a neural network which provides a consistent output given that the input can be in different length vectors? I am currently in a situation where I have sampled a lot of audio ...
899 views

### Quasi-categorical variables - any ideas?

Let's say I'm trying to predict a person's electricity consumption, using the time of day as a predictor (hours 00-23), and further assume I have a hefty but finite amount of historical measurements. ...
5k views

### 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 ...
7k views

### Proper way of fighting negative outputs of a regression algorithms where output must be positive all the way

Maybe it is a bit general question. I am trying to solve various regression tasks and I try various algorithms for them. For example, multivariate linear regression or an SVR. I know that the output ...
4k views

### Best regression model to use for sales prediction

I have the following variables along with sales data going back a few years: date # simple date, can be split in year, month etc shipping_time (0-6 weeks) # 0 weeks means in stock, more weeks means ...
12k views

### Prediction Intervals Using XGBoost

I want to obtain the prediction intervals of my xgboost model which I am using to solve a regression problem. I am using the python code shared on this blog, and not really understanding how the ...
5k views

### Modelling Unevenly Spaced Time Series

I have a continuous variable, sampled over a period of a year at irregular intervals. Some days have more than one observation per hour, while other periods have nothing for days. This makes it ...
2k views

### Fitting lines through large point clouds

I have a large set of points (order of 10k points) formed by particle tracks (movement in the xy plane in time filmed by a camera, so 3d - 256x256px and ca 3k frames in my example set) and noise. ...
1k views

### Interpreting the evaluation result of multiple linear regression

I am learning the multiple linear regression model. I've built a model and using R command: summary(model) I got this result: ...
779 views

### How is the 'feature_importance_' value calculated in sklearn random forest regressor?

I have 9000 sample, with five features, and one output variable (all are numerical, continuous values). I used random forest regression method using scikit modules. ...
2k views

37 views

### What quantile is used for the initial DummyRegressor for Gradient Boosting Regressor in scikit-learn?

According to the documentation of Scikit-Learn Gradient Boosting Regressor: init: estimator or ‘zero’, default=None: An estimator object that is used to compute the initial predictions. init has to ...
158 views

### Chose the right regression analysis

In R I have data where head(data) gives ...
88 views

### how Lasso regression helps to shrinks the coefficient to zero and why ridge regression dose not shrink the coefficient to zero?

How Lasso regression helps feature selection of model by making the coefficient to zero? , I could see few below with below diagram ,can any please explain in simple terms how to corelate below ...
7k views

### Difference between Ridge and Linear Regression

From what I have understood, the Ridge Regression is just having the loss function for an optimization problem with the addition of the regularization term (L2 Norm in the case of Ridge). However I am ...
3k views

### Multi-output regression problem with Keras

number of features: 12 , -15 < each feature < 15 number of targets: 6 , 0 < each target < 360 number of examples: 262144 my normalization: I normalized the features so that they are ...
2k views

### Should the output of regression models, like SVR, be normalized?

I have a regression problem which I solved using SVR. Accidentally, I normalized my output along with the inputs by removing the mean and dividing by standard ...
7k views

### Large mean squared error in sklearn regressors

I'm a beginner in machine learning and I want to build a model to predict the price of houses. I prepared a dataset by crawling a local housing website and it consists 1000 samples and only 4 features ...
55 views

### What method/algorithm for constrained multi-target regression

I am working with three dimensional measurement data and want to model them using a multivariate linear regression. I have already implemented a simple gradient descent algorithm to solve the classic ...
41 views

### How much can bias decrease performance of the network at the beginnng of the training?

I am writing a custom framework and in it I'm trying to train a simple network to predict the addition function. The network: 1 hidden layer of 3 Neurons 1 output layer cost function used is ...
I have a camera system with some special optics that warp the field of view of the camera, dependent on two variables, $\theta_1$ and $\theta_2$. Given a specific configuration of these two variables, ...