Questions tagged [regression]

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

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16
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2answers
11k 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 ...
11
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2answers
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 ...
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4answers
353 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 ...
10
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3answers
21k 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:
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2answers
82 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 ...
24
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3answers
36k 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 ...
21
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3answers
13k 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: ...
13
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3answers
26k views

What does “linear in parameters” mean?

The model of linear regression is linear in parameters. What does this actually mean?
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2answers
4k 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?
6
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2answers
4k 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 (...
8
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2answers
9k views
5
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3answers
703 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. ...
7
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1answer
7k 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 ...
6
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2answers
1k views

How does Implicit Quantile-Regression Network (IQN) differ from QR-DQN?

For several months I browsed the internet hoping to find a user-friendly explanation of the Implicit Quantile Regression Network (IQN). But, it seems there is none at all. How does IQN differ from ...
12
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2answers
2k 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 ...
8
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2answers
1k 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. ...
6
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3answers
20k 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 ...
11
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3answers
1k views

Can regression trees predict continuously?

Suppose I have a smooth function like $f(x, y) = x^2+y^2$. I have a training set $D \subsetneq \{((x, y), f(x,y)) | (x,y) \in \mathbb{R}^2\}$ and, of course, I don't know $f$ although I can evaluate $...
10
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3answers
3k 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 ...
5
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2answers
14k 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 ...
4
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1answer
91 views

Common Techniques to Generate from a Regression Neural Network Model

I am used to train neural networks that are designed for generation, such as GANs or VAEs. I am wondering what are the common techniques to generate data that would minimize the target/energy learned ...
4
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2answers
635 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: ...
6
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3answers
472 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 ...
3
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2answers
167 views

Prediction in Machine Learning

When we use a regression algorithm in out dataset it's because we assume that there is a relation between our input data and some quantitative value. This is expressed as : $y = f(x)+\varepsilon $, ...
3
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3answers
5k 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,...
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2answers
142 views

Chose the right regression analysis

In R I have data where head(data) gives ...
4
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2answers
2k 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 ...
3
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1answer
1k 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 ...
2
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2answers
35 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 ...
2
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0answers
50 views

Modified Voting Algorithm to find the best recommendation

I have to find the best 10 items from the set of items for x number of given features. I don't know what the best recommendation will be. User data is not available to validate it. After reading a ...
2
votes
2answers
918 views

Alternatives to linear activation function in regression tasks to limit the output

I want to know whether there is a way to limit the output of a regression deep model. Suppose that I want my model outputs values which are in a specified range and penalizes the outputs which are not ...
1
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0answers
11 views

Non-parametric regression on set of time series: One model for each or one for all series?

Let's say I have a set of 1D time series which values have been samples in equip-distant time steps with timestamps $1,2,3,...$, they have all the same lengths and are somewhat similar in shape. I ...
1
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3answers
131 views

R-square or adjusted R-square for one variable model?

I have model like y=mx. Since the adjusted R2 tells you the percentage of variation explained by only the independent variables that actually affect the dependent variable and I have only one ...
1
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2answers
2k views

Prediction after one hot encoding

I have a regression model that I want to make prediction based on values that I will get from an end user. In my dataset, I have one categorical variable region ...
1
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2answers
51 views

Noise has 0 mean

Why do we assume that in a dataset the error, presented as the random error term, has mean 0 ? To me seems impossible that every event we can study in everyday life has an error with 0 mean...
0
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1answer
31 views

Can we have a dataset with slight difference in target values for same value of feature variable?

I am trying to generate a dataset which involves 1 feature variable(X) and 1 target variable(y). The feature variable ...
0
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2answers
49 views

Regression vs. Expected value for campaign results

Let's say that I have 100k customers, and I can only send 10k letters for a loan, and I do have information from past campaigns and I know I can expect about 100 responders. If I want to get in my ...