# Questions tagged [linear-regression]

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

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### Loss function for normal distribution regression problem

My project involves training an input of random uniformly distributed data using regression (this is my approach) to output random normally distributed data. The issue with formulating the problem is ...
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### What's the correct cost function for Linear Regression

As we all know the cost function for linear regression is: Where as when we use Ridge Regression we simply add lambda*slope**2 but there I always seee the below as cost function of linear Regression ...
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### data analysis leads to linear regression model: how to proceed with prognosis?

Data analysis of a large dataset of project management data together with working hours led me to a surprisingly simple linear model over the key milestones of all projects. Now I am a bit at loss on ...
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### AI algorithm model that outputs a list of unknown length [closed]

I have a dataset with the following x columns: date time is_weekend is_holiday start_intersection end_intersection The output is a list of intersections, that connect start_intersection with ...
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### Does gradient descent always find global minimum for specific regression type?

From my understanding, linear regression is used for predicting an output based on an input using a linear equation that is optimally fitted to some input data. We choose the best fitted linear ...
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### Multiple Linear Regression for House Price Prediction score is 0.28 [closed]

I am trying to make predictions using this dataset What I have done so far: Dropped the Administrative column Encoded the categorical data using ...
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### Implementation of a perceptron

I want to implement a single perceptron for linear regression using the following formulas: the input data for the first case is one column (x(392, 1); y(392, 1)) and for the second case is (x(392, 7)...
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### One predictor variable and 3 response variable (categorical and continuous) [closed]

If I have predictor variables which are a mixture of continuous and categorical, and a response variable that is continuous. What approach should I apply? Linear regression, logistic regression or k ...
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### The effect of the λ in the Ridge regression

Why by increasing value of λ in Ridge estimator the slope of the line is decreasing? How exactly λ affects to the y = kx + b?
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### Error while calculating accuracy and matrix multiplication in tensor flow code for regression [closed]

I was writing a code for linear regression using tensor flow but I was getting errors while calculating matrix multiplication using tensor flow and while calculating accuracy. ...
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### Can I retrain my best model on all available data? [duplicate]

I split data on Zillow single-unit properties into train-validation-test 70-15-15 and trained a few different sklearn linear models to predict selling price. I chose the best one based on validation ...
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### Group points to reduce data set such that the linear regression stays the same

I have a very long dataset and I'm trying to reduce it by grouping the data in periods of 24 hours. In this way, there will be a single data point that represents that day, but they must yield the ...
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### cost function diverging in batch gradient descent

I am trying to implement the gradient descent method in python. I would like the calculation to stop when abs(J-J_new) reaches a certain tolerance level (i.e. it ...
16 views

### Approximating weight of individual items from sum of their weight

Problem I have a list of orders, approximation of their total weight and list of items they contain. I need to determine approximate weight of individual items. In other words, I have a few thousand ...
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### Linear regression of times series data with heteroskedasticity

I am trying to find out if stock market movements, on average and in extreme conditions, affect gold prices. I am following the regression model proposed by Baur and McDermott (2010) which is given as:...
112 views

### Constraining linear regressor parameters in scikit-learn?

I'm using sklearn.linear_model.Ridge to use ridge regression to extract the coefficients of a polynomial. However, some of the coefficients have physical ...
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### How do I decide on my X and Y variables for the prediction of a coin toss?

So I'm new to data science and was trying to solve a few problems that my mentor gave to me. I came across this question where there are multiple coin tosses and ten of them are recorded. I am ...
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### prove E[(TSS - RSS)/p] > $\sigma^2$ in multiple linear regression

In Intro to statistical learning, Chapter-3 for Linear Regression, in the subsection 3.2.2 , Unit "One: Is There a Relationship Between the Response and Predictors?" , it is mentioned that: ...
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### Linear regression to find differences between model performances

For one of my projects I needed to create classification models for each of many products. In order to see which classifier performs best, I created one SVM, RandomForest and Naive Bayes model for ...
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### predicting average time with regression

I have a trip duration dataset that looks like this: I want to use other parameters to predict the waiting time (wait_sec). The waiting time refers to the time the vehicle is stuck in traffic or so. ...
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### Why is linear regression not doing worse with a low weighted attribute?

I've been able to build a few linear regression models that can predict a material strength quite well: minimum RMSE of 17.95 using 11 attributes that I have selected from 159 original attributes. The ...
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### Linear models: Imputing missing not at random

This question is a continuation of a similar question for linear models instead of Tree-based model. Given that linear models (e.g. lasso, ridge, Linear regression, elastic net, etc.) can't handle ...
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### Is it usual for Scikit learn's standard scaler to cause non-invertibility?

For example, I am trying to perform linear regression on the following set of data Data examples: $X = [[1, 20], [3, 40], [5, 60]]$ (each row is an example, there are three examples, each with a ...
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### Definition of linear model

I am new to machine learning and am a bit confused about the definition of a linear model. I've searched many sources and the most common definition is: The term linear model implies that the model ...
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### Statistical significance of SVD least squares

I was not able to find any info on how least squares using singular value decomposition should be statistically evaluated. I have a dataset for which I did both multivariate regression and regression ...
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### Best approach for univariate time series predictions?

I have a univariate time series. where I'm trying to predict a current value of a variable based on the previous 10 values of the same variable. I tried three approaches: 1- linear regression where I ...
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### Does anyone know of literature regarding a Neural Net boosted GBM?

For obvious reasons, most GBMs created in the private sector are tree boosted. Occasionally, one might want a linear boosted GBM so that the residual models collapse into a simple linear combination. ...
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### Extremely negative r^2

I use a linear regression to predict house prices (https://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview). My linear regression sometimes works great with R^2 of 0.8 and ...