Questions tagged [regression]

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

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14 views

Neural-Networks - preferred method for training, classification v.s. regression

As a conclusion of their paper "Efficient Backprop" (http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) (§10 Discussion and Conclusion), LeCun and others conlude that the preferred method for ...
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11 views

Why does reducing polynomial regression to linear regression work?

Getting into machine learning, have a reasonable background in statistics and understand the basic principles of linear algebra (matrix multiplication etc.) - but am having a damn hard time figuring ...
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2answers
140 views

Compare Coefficients of Different Regression Models

in my project, I am using asuite of shallow and deep learning models in order to see which has the best performance on my data. However, in the pool of shallow machine learning models, I want to be ...
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1answer
16 views

variables selection in regression models

I develop price prediction data model using multiple linear regression, ridge, lasso and elastic net regression, initially I had 215 variables. after creating models I ran a python code to check how ...
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1answer
17 views

Confidence intervals in multivariate linear regression

I am fitting my data to a multivariate linear regression $Y = BX + \Xi$, where the response is bivariate $Y\in R^{n\times 2}$, and the predictor is uni-variate but elevated to the projective plane to ...
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1answer
26 views

On regression to minimize log distance rather than distance

Suppose I have a lot of points $ x_i \in \mathbb{R}^N $ with corresponding non-negative labels $ y_i \in \mathbb{R} $ and I want to do regression and make a prediction on some new datapoint $ x^* \in \...
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29 views

How ARIMA Model works? [closed]

I want to learn the ARIMA model to predict a series of time data as mentioned below The Dataset looks like this: ...
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1answer
18 views

Quantifying a trade-off

Let our data be a set of 2d points sampled from a line $ax+by=1$ (that we do not know) plus some noise $\nu$. This is meant to represent a frontier of development - we can build a car with a ...
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1answer
45 views

Decision tree regression: Polynomials unnecessary?

I am testing out different models for a regression task. When using OLS, Ridge and Lasso, I use different polynomial degrees of the explanatory variables. Example: For two variables x and y, degree 2 ...
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1answer
29 views

Understanding orthogonal regression

In orthogonal regression, we are trying to minimize the distance from each data point $(x,y)$ to the fitted model. My question is, how come that there is a distinction between independent and ...
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1answer
13 views

Physical modelling with neural networks - single output + stack ensemble vs multi-output

We are trying to replace an existing physical model (8 inputs/7 outputs) with artificial neural networks. The physics behind the existing model is mainly thermodynamics of humid air for air ...
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1answer
27 views

How to use CNN to deal with a 2D regression problem?

I have seven measurements (Obs1-7), each measurement has the dimension of [x,y,t] where x and y are coordinates and t is time. Now I want to build a model that uses the first 6 measurements to predict ...
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1answer
49 views

Predicting a month's data

The Dataset looks like this: ...
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1answer
22 views

Transforming negative correlated non linear variable to linear positive correlated variable

At my office, I am stuck in a weird situation. I am asked to perform a regression algorithm on the data, in which the target variable is continuous having values range between 0.6 to 0.9 with 8 digits ...
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1answer
26 views

Ridge regression model creation using grid-search and cross validation

I created python code for ridge regression.For that I used cross validation and grid-search technique in together. i got output result. I want check whether my regression model building steps correct ...
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1answer
20 views

Need of Weighted Mean Squared Error

We have MSE and RMSE as evaluation metrics for regression problems. I have for some problems people use Weighted Mean Squared Error (WMSE) as the evaluation metrix. Below is the WMSE formula: Can ...
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1answer
47 views

How is the 'feature_importance_' value calculated (in sklearn modules) for each variable in a 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. ...
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5answers
299 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 Parameter for the ...
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1answer
27 views

How to automatically test for the best parameters for transformed independent variable in linear model

Let's assume that I have a linear model with $k$ variables: $y = \beta_0 + \beta_1\cdot x_1 + \dots + \beta_k \cdot x_k$. Now, I want to add variable $x_{k+1}$, but, according to domain knowledge, ...
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1answer
33 views

In which cases are non-linear learning methods preferred than logistic regression in classification problems?

We know that neural networks and other learning methods can have better performance relative to logistic regression in some non-linear classification problems. But, it is known too that logistic ...
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2answers
980 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 ...
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1answer
26 views

How can I use time stamps for classification?

My data consists of entries when an event is True, namely, when a train crossing is down. So I will have entries within a day like so (just examples): ...
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2answers
41 views

Chose among highly correlated variables

I am working on a Kaggle dataset and I am trying to build a predictive model for the "Chance of Admit" (dependent variable) of students to the university of their interest. Below you can find the ...
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0answers
14 views

regression model assumption [migrated]

I try to check whether my regression model is follow regression assumption or not? for that I did below python code but response is error. can someone explain how it wrong ...
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1answer
38 views

Differences between normalization and standarization in multiple regression

Consider the following question regarding multiple regression 1) Can someone explain why we have to transform dependent variable using log-transformation (Normalization) when appear positive skewed y ...
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1answer
29 views

Regime classification pre model training?

i don't have a formal background in this so please bear with me. This is what my dataset looks like : I'm interested in modeling the first variable using the rest as explanatory. A simple OLS yields ...
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1answer
25 views

How can I compare my regressors?

I am trying to build a regressor for a dataset which gives info about students' school performance and the probability of getting admitted in the University of their choice. The first 5 observations ...
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1answer
25 views

Terminology - regression with one output and multiple output variables

I am trying to predict the response when the input is represented by Fourier transform. These form the features and are typically represented as a vector, $x_1,x_2,...,x_d$ where $d$ is the length of ...
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9 views

Neural Network innaccurate when output is a smaller value

As you can see from the images below, the majority of the data is between 0-2, meaning that for that segment my error is very high but for larger values my error seems to be more insignificant. How ...
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3answers
93 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 ...
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2answers
94 views

Tensorflow - simple multi-layer perceptron not stabilizing around mean of normally distributed y-values

I'm building an FX trading model where I'm trying to predict the +/- movement of a currency pair 5 minutes into the future. I've had some promising results adapting the model as a classifier (i.e., ...
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0answers
16 views

How can I improve my non linear multi dimensional regression? 8 parameters and 8 sets of data, 4 variables

I have this system of equations that I need to fit 8 parameters with 8/maybe 9 sets of experimental data: My response variable is [DCF], I have three independent variables: [O_3], [FeOOH] and time. ...
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0answers
18 views

Calculate a Rank function from Regression features

I am using 3 features (x1, x2, x3) for regression. Some of my features are continuous some are categorical. My dependent variable are lets number of bookings. And I can predict the number of bookings....
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2answers
49 views

Get the Polynomial Equation with Two Variables in Python

TL;DR predict "price", given "length" and "wandRate" I have some time-series data where the dependent variable is a polynomial result of 2 independent data points. Here is a snippet: This is ...
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1answer
20 views

how to know what my data are uninformative and that machine learning will not work with it?

following this question, I'm making a data analysis again because I tried to use machine learning algorithms like Random forest to predict a value from certain features but it didn't work for me. I ...
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2answers
65 views

Loss function for multivariate regression where relationship between outputs matters

I am attempting to build a sequential model with Keras (Tensorflow backend) that has multiple outputs. My targets are proportions of a whole so each observation is an array like [0.5, 0.25, 0.15, 0.1]....
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1answer
21 views

How to pass linear regression weights to Xgboost regressor?

I'm trying to build an xgboost regressor or a catboost regressor for a task. I have a working linear regression model. I also trained an xgboost regressor model for the task but it was worse than the ...
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1answer
31 views

How to predict the dealer whether pick up the goods next month?

Here, I want to predict dealers(about 600) whether pick up the goods(about 30) next month. As you see, there are about 18,000 possibilities and it's difficult to predict. By the way, now I have ...
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1answer
26 views

Which kNN model to chose?

I am trying to tune the "n_neighbors" for a kNN model andI have the following problem : Based on the mean cross validation score the optimal kNN model should be the one with 10 neighbors. On the ...
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2answers
54 views

why does making the target variable normally distributed helps?

while working on some regression problems I have found that if the target variable is skewed, making it normally distributed(using transformations) almost always helps. Why is that? Should we also ...
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9 views

Handling data with exactly same features but slightly different outputs? [Regression Problem]

I have a dataset where if I remove two attributes, in some cases remaining features are exactly the same but target value changes slightly. In my test set, I don't have those removed attributes so I ...
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1answer
24 views

LSTM with linear activation function

I'm trying to do multi-step regression and I use an output layer: LSTM(1, activation='linear', return_sequences=True) Is this the wrong way of achieving this? Should I use a TimeDistributed(Dense(1))...
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1answer
15 views

Upper bound on 'relatedness'?

We have ~100 answers to a questionnaire with five questions (Q5). Independently from that, we have about 50, somewhat overlapping, features describing the people who answers the questions (F50). After ...
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24 views

Need help with method=“leapSeq”

Here is my code: ...
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0answers
35 views

ARIMA: How to understand performance of the model?

I am new to use of ARIMA model and after working on it for a couple of days and doing research - I'm not sure how to interpret the performance of my model... Here is what the ...
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0answers
21 views

Curve fitting through cloud of daily values using machine learning

I want to plot laboratory values (SCr_v) over time and find the best fitting regression curves (see plot 1 and 2). I don't want to restrict myself to a specific model if possible. Is there a function ...
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2answers
126 views

Are stationarity and low autocorrelation the prerequisite of regression model?

As said in the title, are stationarity and low autocorrelation the prerequisite of general / linear regression model ? That is, if a time series is non-stationary or has large autocorrelation, would ...
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32 views

what should I do if my Neural network model stuck on high value loss?

I'm using neural nets in my projects. It's a regression problem where i have 3 features and I'm trying to predict one continuous value. I noticed that my neural net start learning good but after 10 ...
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1answer
60 views

Tensorflow model works for classification but not for regression (all predictions equal the output layer bias)

I'm trying to build a model for FX prediction. It's giving some promising results for classifying each period as buy/sell/neutral. When used as a classifier, actual returns are converted to 0, 1, or ...
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1answer
115 views

Calibrating Correlation

I am facing a weird problem in my on going project and thought if someone here could help me out with this. Actually I have large data set. I have to perform a regression task on top of that. While ...