Questions tagged [regression]

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

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24
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3answers
35k 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: ...
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 ...
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?
13
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3answers
47k views

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 ...
12
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2answers
1k views

Airline Fares - What analysis should be used to detect competitive price-setting behavior and price correlations?

I want to investigate price-setting behavior of airlines -- specifically how airlines react to competitors pricing. As I would say my knowledge about more complex analysis is quite limited I've done ...
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 ...
11
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3answers
20k views

What does “baseline” mean in the context of machine learning?

What does "baseline" mean in the context of machine learning and data science? Someone wrote me: Hint: An appropriate baseline will give an RMSE of approximately 200. I don't get this. Does he ...
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 ...
11
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3answers
1k views

What regression to use to calculate the result of election in a multiparty system?

I want to make a prediction for the result of the parliamentary elections. My output will be the % each party receives. There is more than 2 parties so logistic regression is not a viable option. I ...
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
20k 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:
10
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2answers
27k views

Multivariate linear regression in Python

I'm looking for a Python package that implements multivariate linear regression. (Terminological note: multivariate regression deals with the case where there are more than one dependent variables ...
10
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2answers
8k views

Stochastic gradient descent based on vector operations?

let's assume that I want to train a stochastic gradient descent regression algorithm using a dataset that has N samples. Since the size of the dataset is fixed, I will reuse the data T times. At each ...
10
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2answers
3k views

What to do when testing data has less features than training data?

Let's say we are predicting the sales of a shop and my training data has two sets of features: One about the store sales with the dates (the field "Store" is not unique) One about the store types (...
10
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1answer
13k views

Interpreting the Root Mean Squared Error (RMSE)!

I read all about pros and cons of RMSE vs. other absolute errors namely mean absolute error (MAE). See the the following references: MAE and RMSE — Which Metric is Better? What's the bottom line? How ...
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 ...
9
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1answer
32k views

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 ...
9
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2answers
2k views

Predict task duration

I'm trying to create a regression model that predicts the duration of a task. The training data I have consists of roughly 40 thousand completed tasks with these variables: Who performed the task (~...
9
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1answer
9k views

How do I implement the sigmoid function in Octave? [closed]

so given that the sigmoid function is defined as hθ(x) = g(θ^(T)x), how can I implement this funcion in Octave given that g = zeros(size(z)) ?
9
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1answer
2k views

Can training label confidence be used to improve prediction accuracy?

I have training data that is labelled with binary values. I also have collected the confidence of each of these labels i.e. 0.8 confidence would mean that 80% of the human labellers agree on that ...
9
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3answers
5k views

Export weights (formula) from Random Forest Regressor in Scikit-Learn

I trained a prediction model with Scikit Learn in Python (Random Forest Regressor) and I want to extract somehow the weights of each feature to create an excel tool for manual prediction. The only ...
8
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2answers
1k views

Can overfitting occur in Advanced Optimization algorithms?

while taking an online course on machine learning by Andrew Ng on coursera, I came across a topic called overfitting. I know it can occur when gradient descent is used in linear or logistic regression ...
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2answers
9k views
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. ...
8
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2answers
2k views

Dealing with feature vectors of variable length

How does one deal with a feature vector that can vary in size? Let's say per object, I calculate 4 features. In order to solve a certain regression problem, I may have 1, 2, or more of these objects (...
7
votes
1answer
9k views

Why should I normalize also the output data?

I'm new to data science and Neural Networks in general. Looking around many people say it is better to normalize the data between doing anything with the NN. I understand how normalizing the input ...
7
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2answers
5k 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 ...
7
votes
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 ...
7
votes
1answer
14k views

How do i pass data into keras?

I am currently struggling to understand how i should train my regression network using keras. I am not sure how I should pass my input data to the network. Both the input data and the output data is ...
7
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1answer
7k views

How to get a confidence score for predictions?

In a regression problem, is it possible to calculate a confidence/reliability score for a certain prediction given models like XGBoost or Neural Networks?
7
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2answers
3k 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?
7
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2answers
987 views

Theoretical bound - regression error

The Bayes error rate is a theoretical bound that determines the lowest possible error rate for a classification problem, given some data. I was wondering whether an equivalent concept exists for the ...
6
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3answers
19k 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 ...
6
votes
2answers
4k views

Can Neural Networks be trained to smooth values / output the average?

Let's say we have a neural network with one input neuron and one output neuron. The training data $(x, f(x))$ is generated by a process $$f(x) = ax + \mathcal{N}(b, c)$$ with $a, b, c \in \mathbb{R}^...
6
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2answers
1k views

What are some resources to test your data science skills?

I can't get a job to save my life so I am guessing my lack of skills is an issue. I've been doing a lot of reading on statistics and I am getting antsy - I want to move from theory to application and ...
6
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2answers
1k views

Improve a regression model and feature selection

I am working on Azure ML Studio and try to create a regression model to predict a numerical value. I will try to describe my features and what I have done until now. My data with about 3 million rows ...
6
votes
3answers
459 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 ...
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 ...
6
votes
1answer
190 views

Regression model for a count proces

In R I have data where head(data) gives ...
6
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2answers
4k views

Dummy coding a column in R with multiple levels

I have a dependent variable measuring the net revenue. One of the major predictor affecting this is "product" i.e. the product sold to the customer. My randomly sampled dataset contains 1.4 million ...
6
votes
3answers
126 views

Regression: How to deal with positive skewness in continuous target variable

I'm working on a regression problem. My aim is to "learn" the distribution of a continuous target $y$ as good as possible to make predictions. My model looks like: $$y_i=\beta X_i + u_i.$$ $y$ is ...
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 (...
6
votes
3answers
2k views

How to estimate the variance of regressors in scikit-learn?

Every classifier in scikit-learn has a method predict_proba(x) that predicts class probabilities for x. How to do the same thing ...
6
votes
2answers
5k views

Regression model to predict probability of rare event

I have a dataset with around 900.000 records, around 1000 of which are marked as positive (the studied event occurred). The probability of the event occurring is always low (i.e. < 0.1), and I ...
6
votes
3answers
141 views

Can we quantify how position within search results is related to click-through probability?

Suppose, for example, that the first search result on a page of Google search results is swapped with the second result. How much would this change the click-through probabilities of the two results? ...
6
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2answers
115 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 ...
6
votes
1answer
716 views

Choosing best methods for estimating the unknown parameters in a linear regression model

Given some dataset for prediction, for eg say I have different housing price prediction dataset: dataset 1 : 100 training and 100 testing sample, 50 feature dataset 2 : 100 training and 100 ...
6
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2answers
2k views

Data Augmentation for Regression

I want to use deep learning for regression. However, the number of training samples is not large. In image processing, some new samples are generated on the basis of initial data through tasks like ...
6
votes
2answers
1k views

Optimizing CNN network

I am currently trying to recreate the result of this paper, in which they do feature extraction from a "spectogram" of log-melfilter energies.. Since the paper doesn't state what kind of feature I ...

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