Questions tagged [regression]

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

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9 views

Removing the effect of Time series X on time series Y, when their relation is unknown

I am working on a dataset of 6 years measurements of a water quality parameter called 'chla' ( parameter 'X') measured by a sensor for each year from May to October. The parameter has its own trend ...
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36 views

What is the underlying difference between linear and non-linear relationship?

Study on linear correlation coefficient and nonlinear correlation coefficient in mathematical statistics WANG Ting, ZHANG Shiqiang Studies in Mathematical Sciences 3 (1), 58-63, 2011 From the above ...
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If mean absolute loss is not differentiable, how it can be used in neural networks? which majorly are trained using back-propagation

If Mean Absolute Error (MAE) loss is not differentiable, how can it be used in neural networks? which majorly are trained using back-propagation I am wondering if MAE is not differentiable how they ...
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23 views

Computing Rare values After SMOGN - Imbalanced Regression

I am dealing with a regression problem where I have the phenomenon "Imbalanced Regression". In my problem, the most relevant events are scarcely represented. In order for me to evaluate my models' ...
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49 views

How to make a classification problem into a regression problem?

I have data describing genes which each get 1 of 4 labels, I use this to train models to predict/label other unlabelled genes. I have a huge class imbalance with 10k genes in 1 label and 50-100 genes ...
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41 views

Time series regression

I m new in the domain of machine learning. I m here to ask for some elucidation. I have a data set presented as a time series( from a strain sensor coming from a wind turbine). In this time series, we ...
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1answer
13 views

What is the best approach for send time optimization? [closed]

I could no find a lot information about how the companies doabout send time optimization, either for push notifications or email campaigs. having historical data about clicks and sends what would be ...
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1answer
28 views

r2 for regression models it is a score or error? [closed]

some places I have seen it is called as score and some other place as error. Lets suppose r2=0.83 means that score = 83% and Error= 17% or vise versa
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30 views

Meaningful predictive analytics for small (n=114) dataset with just 1 explanatory variable and 1 response variable?

I am given an Excel pivot table that aggregates data from a somewhat sizable data source (a database table with 1.9m records and another of about 490k). The data within the Excel file consists of 3 ...
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8 views

Multi Output Regression to Compare the Difference of the Network Outputs

I would like to build a network with Keras / Tensorflow, where I read in several features and receive two outputs (actual value / setpoint). I would like to compare these to determine whether they ...
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21 views

Improve elastic-net feature selection to reduce the file-size burden on the system

I have written a feature selection program in R using elastic-net regression. My training data has around 65 samples with 500K features. I am using 500 bootstraps to resample the data and during each ...
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How effective is Moore Penrose for solving regression problems with overdetermined system of equations?

For regression problems with #Predictors > #observations, I recently read about Moore Penrose (pseudo inverse method) which solves the problem of non invertible matrix in OLS for regression problems. ...
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40 views

using feature selection to improve model performance

I have a highly sparse dataset that I am using to predict a continuous variable via a random forest regression. I have achieved an acceptable level of performance following cross-validation, and I am ...
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21 views

I don't understand RidgeCV's fit_intercept, and how to use it for my data

Alright, I have an assignment that makes me calculate weights for a function with different terms. At first, I thought I might just leave the weight for the term $1$ out, and instead use the intercept....
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1answer
17 views

How do we determine what is learnt by a ML model?

I am trying to investigate/justify the output of a random forest regression model for a financial problem, where justification of the output is also important. In that context, for the random forest ...
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34 views

Why there is very large difference between cross validation scores?

I have a very simple regression model and I am doing the cross validation. When cv=10 the highest score i got is 60.3 and lowest is -9.7 which is useless. Average will be 30. No of row data set= ...
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21 views

Ideal strategy for multi variable regression attempting to maximize the target

I am trying to implement machine learning for the following data Data Input What I am trying to achieve is to keep the ad bid & cost per sale as low as possible while increasing sales. This is ...
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1answer
19 views

How does Excel obtain the R² determination coefficient in an exponetial regression?

How does Excel obtain the R² determination coefficient in an exponetial regression? This may seem a silly question but Excel actually shows a R² coefficient for non-linear regressions. How does Excel ...
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75 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 ...
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1answer
41 views

Covariance matrix in linear regression

I have read about linear regression and interpreting OLS results i.e coefficients, t-value, p-value. But unable to find any material related to covariance matrix in linear regression. I was reading ...
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1answer
25 views

Can one property name be used twice in the same branch of a DecisionTreeRegressor?

I am using this dataset for the analysis (Generated using make_regression of sklearn library) I was trying to learn the DecisionTreeRegression algorithm of sklearn library. I used the following code ...
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1answer
77 views

Linear regression assumptions

I have read that we make the following assumption for linear regression: 1. Linearity (correct functional form) 2. Constant error variance (homoskedasticity) 3. Independent error terms (no ...
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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 ...
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47 views

l2-regularized regression in keras

I just started with Keras/Tensorflow and I am trying to reproduce results from one paper, where authors minimized the following loss function \begin{align} \min_{w \in R^d} \frac{1}{n} \sum_{i \in [n]}...
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139 views

Does ridge regression always reduce coefficients by equal proportions?

Below is an excerpt from the book Introduction to statistical learning in R, (chapter-linear model selection and regularization) "In ridge regression, each least squares coefficient estimate is ...
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1answer
48 views

Getting the leave-one-out error on least square regression to fit polynomials

I need to implement least square regression to fit polynomials of degree 1-27. I then need to get the leave-one-out error (kfold cross validation where k = n). After doing a lot of research it seems ...
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48 views

Is it reasonable to use the output of the sigmoid function as the win rate prediction?

I'm working on a project which is predicting the win rate of one team or one person. (could be any kind of sports like baseball, basketball or e-sport games) The data I have is more like a ...
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25 views

Model Predictive Power and better prediction of 1 or 0 in Python scikitlearn?

I have 2 questions about Logistic Regression model in Python scikit-learn: Which statistic can show me model predictive power ? Which statistic can show me whether my model better predict event 1 or ...
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LSTM Regression TS with >50% of zero outcomes for y

Modelling multivariate Time series with LSTM, the y of the TS are on >50% consist of zeros. the same is true for features I use loss = 'mean_squared_error', optimizer =Adam, and make grid search ...
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2answers
59 views

Is there a possibility that there is no relationship between some inputs and outputs?

I have a general question that comes to my mind, I'm doing machine learning projects and I took a look at many datasets and worked with, mostly there are already famous datasets that everyone uses. ...
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1answer
165 views

Importance of variable to implement in Logistic Regression model in Python sklearn?

Logistic regression using Python sklearn In Logistic Regression model we can implement variables which meet 3 conditions: independent variables have to be correlated with dependent variable ...
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16 views

Linear Mixed Model vs Variable Interaction

I am studying relationship between 'Y' and two continuous vars(X1, X2) + one categorical var (C). I think the categorical variable not only influence 'Y', but also changes coefficients of continuous ...
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33 views

Which algorithm to use to identify clusters with a similar value?

Here, an example of my problem: 10000 observations of people with several features [age, gender, region, number of sons, ...] and a value to predict "income". There is not a general relationship ...
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1answer
31 views

Continuous Estimated Time of Arrival

I'm trying to create a model for when a shipped product will arrive at its destination. There are several stages the delivery goes through, so it's not just drive time from point A to point B. My ...
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Categorical feature as output and perform a classification

I have a database in which the output feature Y is categorical, for example (oversimplification) ...
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1answer
204 views

Low memory error while performing degree 2 polynomial regression on (3000*1835) sized array

I am working on a problem to predict the revenue, a film will generate. Some of the features available in the data set are json collection for the crew, cast which worked in the film. I applied ...
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Physical interpretation of contribution of a feature in regression

I have built a random forest regression model for credit underwriting. But the business doesn't appreciate a black-box approach. So, using treeinterpreter, I have ...
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1answer
37 views

Solve an equation using machine learning [closed]

Imagine we have the following equation: y=xz. We have y but not other ones. Note that y is like a matrix and we could as many sample we want. It is the values obtained from sensors. This means it ...
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2answers
284 views

fbprophet - adding regressor

During linear regression classes in academia, it is taught that including trivial/irrelevant features to the model decreases its ability to predict more accurately. In fbprophet, there is this ...
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1answer
32 views

Can we optimize regression problems that have categorical variables by encoding them if on the other hand we are inserting multicollinearity? [duplicate]

Can we optimize regression problems that have categorical variables by encoding them if, on the other hand, we are inserting multicollinearity?
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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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What toolbox to use to create multi-output random forest(reggression) with custom spltting function at each node?

I am trying to implement "Real Time Head Pose Estimation fromConsumer Depth Cameras" by Fanelli et al. I need to train a random forest(regression) with the following criterion The predicted output is ...
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1answer
43 views

Differentiable loss function for ranking problem in regression model

In regression problem, we may need a loss function to measure the relative ranking accuracy between targets $y$ and predicted values $y_{pred}$. Abviously, the simple MSE does not consider such ...
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54 views

How to predict an outcome within a specific time window?

I have a dataset which has around 10K records. My objective is to predict whether the customer will churn or not. Binary classification problem with each class representing around 55:45 proportion ...
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1answer
90 views

Using time series to predict house prices vs. multiple linear regression

Re-post. Machine Learning Courses often teach house prices prediction using multiple linear regression - when we want to predict the value of a variable based on the value of two or more other ...
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When using Partial Least Squares (PLS), does the response distribution need to be symmetric?

I've been working through the exercises in the Applied Predictive Modeling book. The solution guide for Chapter 6 exercise 2 states that a log transformation is required to transform the response ...
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1answer
28 views

Identifying and Accounting for trend/seasonality in Predictor Variables

I'm currently working with a dataset that has been collected over several years, and I suspect my predictor variables are changing over time for their predictive power. I could go back year by year ...
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2answers
122 views

Extract relevant features from time series data

I have a time series data set from a sensor and the task is to predict the time before a failure event is occurred. The data set has one feature and has almost 20 million rows. This is a regression ...
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1answer
24 views

How can you adjust a prediction based on features in the future being different than predicted?

I have a model that takes mostly cumulative data, and makes a prediction. It's not baseball, but I'm using this as a pretty accurate analogy. You put in all the totals so far, and it make a prediction ...
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13 views

Getting bad predictions for high true values of target variable

I am working on a counterfeit medicine sales prediction regression model. As the relationship between target & response variables is non-linear I used tree based regressors random forests and XGB. ...

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