Skip to main content
Data Science

Questions tagged [linear-regression]

Ask Question

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

772 questions
Newest Active Bountied Unanswered
Filter by
Sorted by
Tagged with
0 votes
1 answer
262 views

I'm new to data science, and kinda confused with the workflow and steps to make a model. Before learning the math and concepts behind the algorithms like SVM, linear regressions, etc, I would just ...
Justin Jonany's user avatar
  • 51
1 vote
1 answer
104 views

I'm a beginner, and I'm wondering whether a logistic regression in a nut-shell is just normalizing a linear regression? Correct me if I'm wrong, but I came to this conclusion because the predicted ...
Justin Jonany's user avatar
  • 51
1 vote
2 answers
941 views

Why is it difficult to use a linear regression model for the classification problems?
user avatar
1 vote
0 answers
59 views

I am currently training some machine learning models to predict the 28-day compressive strength of cement, a continuous real-valued variable. The available dataset comprises samples from three ...
Felipe's user avatar
  • 21
0 votes
1 answer
83 views

I have a set of pressure datasets from a mechanical device that frequently moves around the country. I also have several sets of environmental data (Altitude, ambient temperature etc.) from those ...
PressureQuery's user avatar
  • 1
2 votes
2 answers
728 views

Another test Q I couldn't answer - We have marks of students belonging to 3 sections - A,B,C and two genders - M & F. Which regression model will not be able to estimate all the parameters? 1 ) ...
a_jelly_fish's user avatar
  • 227
0 votes
1 answer
48 views

The input of my neural network consists of 20 features, whereas the output consists of 20,000 of them (predicting a "quantum classical shadow" based on a few parameters: the rotation angle ...
avpol's user avatar
  • 21
0 votes
1 answer
60 views

I am trying to predict the target variable and finding the difference from actual variable using polynomial regression. However predicted variable is an array of 3 dimension with the shape as (13159,3)...
Hariprasad Rao's user avatar
  • 1
0 votes
1 answer
146 views

I know that having correlated attributes violates the linear model assumption of independent attributes, and I'm not interested in creating a more sophisticated model to tease apart the dependent ...
Brett L's user avatar
  • 3
1 vote
1 answer
141 views

I've a very basic question about cost functions. I'm studying gradient descent and there we're using partial differentiation of features "Theta". But isn't the cost function an absolute ...
MLENGG's user avatar
  • 11
0 votes
2 answers
204 views

I know that Gradient Descent is an optimization algorithm used for optimizing the cost of the loss function. Does Linear Regression model of the sklearn package use ...
mainak mukherjee's user avatar
  • 3
2 votes
2 answers
2k views

When evaluating the output from a linear/ridge regression model, I have taken the residuals between the predicted and test data. This gives me a normal distribution when I plot this data as a ...
amy_hislop's user avatar
  • 23
0 votes
0 answers
67 views

My understanding is that it can make sense to transform the outcomes of a linear regression model to make them more normally distributed. That's because it could 1) help me find more linear ...
Gwater17's user avatar
  • 101
0 votes
1 answer
173 views

I'm trying to make a sales prediction using the column X = item_amount and y = item_price_total, I'm confused whether it's okay to normalize data on the dependent variable using minmaxscalar? With the ...
Fatur's user avatar
  • 1
1 vote
1 answer
1k views

Why are the cost function and mean squared errors called the same thing? WHEN THE COST FUNCTION IS 1/2M AND THE MSE IS 1/N. AND M=N
Rubayet Alam's user avatar
  • 11
0 votes
1 answer
368 views

Why we need a solver like bfgs in LogisticRegression unlike LinearRegression? Don't we have a close form like LinearRegression?
Mahdi Amrollahi's user avatar
  • 283
2 votes
1 answer
96 views

I was running a Linear Regression with Wooldridge dataset named GPA2, which is found on Python library named wooldridge. I tried two linear regressions. The first: ...
dsbr__0's user avatar
  • 191
0 votes
1 answer
41 views

Question: I am not sure how to describe the sample graph attached. Can you please help me identify the type of plot and how to statistically measure the relationship between the dependent variable (Y-...
Leo82's user avatar
  • 1
1 vote
1 answer
98 views

given the following dataset $S = \{(0,1),(1,1),(1,2)\}$ and the regularized problem $$\sum_{i=1}^3 (y_i - w_1 x_i - w_0)^2 + \lambda w_1^2 \quad \lambda = 1 $$ i was tasked with finding the optimal $...
kal_elk122's user avatar
  • 33
1 vote
2 answers
532 views

I searched this question and the answer I got was about a general regression model, rather than a single variable, linear regression model. If you increase the number of variables, you could fit a ...
Dietzsche Nostoevsky's user avatar
  • 11
0 votes
1 answer
66 views

I’ve created a linear regression but my predicted output is usually too low for true high values and too high for true low values. I’ve tried introducing a pipeline where I use polynomial features, ...
Tareq A.'s user avatar
  • 1
1 vote
2 answers
71 views

I have a simple linear function y = w0 + w1 * x, where w0 and w1 are weights, And I'm trying to implement a gradient descent for it. I wrote the function and tested in on the data(a dataset of two ...
Clarify's user avatar
  • 13
0 votes
1 answer
82 views

I have weekly time series data for the last 2 years with variables "week", "marketing_spend", "web_traffic", and "revenue" ...
sdave's user avatar
  • 101
0 votes
1 answer
515 views

I want to predict temperature when time (datetime type, hourly data for five months) and humidity is given. Before starting in python, I created a regression model in excel. But instead of predicting ...
Scholar7's user avatar
  • 1
0 votes
1 answer
163 views

I am going to build the model (e.g. multiple linear regression) to predict the appartment cost in my city. First I have to find outliers in training data. For this task RANSAC regression algorithm ...
Irina Svist's user avatar
  • 1
0 votes
2 answers
451 views

Explainable AI can be achieved through intrinsically explainable models, like logistic and linear regression, or post-hoc explanations, like SHAP. I want to use an intrinsically explainable model on ...
Connor's user avatar
  • 701
0 votes
2 answers
98 views

As part of a statistical learning research paper I am collaborating on, I am running/fitting two hundred sixty thousand different LASSO Regressions on the same number of different randomly generated ...
Marlen's user avatar
  • 167
1 vote
1 answer
593 views

I'm trying to find the function for this visualization: I would like to get feedback if I'm taking the right approach. My approach: These data points are created by a person. They are two ...
Stanko's user avatar
  • 111
0 votes
1 answer
741 views

I'm a newbie at data science and I want to ask how can I predict a set of coordinates from a set of input coordinates? That is (x1, y1) -> (x2, y2). To give a ...
tayron.vikranth's user avatar
  • 1
0 votes
1 answer
66 views

I'm trying to implement the simplest possible machine learning algorithm which is linear regression. But I'm having trouble because the loss function is not converging. Please can you look at my ...
yishairasowsky's user avatar
  • 157
0 votes
1 answer
45 views

All of the code in this question can be found in my GitHub Repository for this research project on Estimated Exhaustive Regression. Specifically, in the "Both BE & FS script" and "...
Marlen's user avatar
  • 167
0 votes
1 answer
116 views

Goal - To train a Linear regression model for climatic studies. Planned features: - Temperatures, Latitude, Longitude, Day Number (1st February = 32) Would it be correct to include day number like ...
Pixel_Bear's user avatar
  • 3
0 votes
1 answer
34 views

I am doing an analysis of aircraft data and I want to see how much fuel is burnt on landing. There are 2 main factors aircraft type and landing time (ie. time elapsed) However there is a cheeky third ...
Jay Ramsay's user avatar
  • 23
0 votes
1 answer
55 views

Scikit learn has a make_regression data generator. Can someone explain it to me like I'm 5 what is meant in the help docs by "The input set can either be well ...
Snehal Patel's user avatar
  • 23
0 votes
1 answer
164 views

Consider a simple linear regression problem where: X = [1,2,3,4,5,100,200] Y= [2,4,6,8,10,200,400] Clearly, the relationship is of the form $y=2x$; While trying ...
butwhy's user avatar
  • 33
0 votes
0 answers
38 views

Let's say we have some data : input data X with shape (1, N=100), this will be duplicated 1000 times. target data Y with shape (S=1000, N=100). We have 1000 experimental data points, samples. My ...
user143751's user avatar
  • 1
2 votes
1 answer
87 views

I am having difficulty achieving the same result as in sklearn while implementing linear regression model from scratch. After adjusting the learning rate, I obtained an AUC of 0.694 for this binary ...
Kyv's user avatar
  • 151
0 votes
1 answer
35 views

I have a dataframe with 50 rows (one row for each US state), and about 20 columns with different attributes with state related data. I'm looking to build a linear regression model to predict ...
Kate's user avatar
  • 1
0 votes
1 answer
300 views

How to go about selecting an architecture for a dataset with 80 datapoints and 9 features for a regression model? Working on the Desarhnais dataset, with "Effort" as the target variable. ...
user17420392's user avatar
  • 1
0 votes
1 answer
1k views

It's a silly problem, I know, but it's getting my nerves. Everything seems fine, but I cannot get the line to show on the plot. I've put it in a public Google notebook, for your convenience. t ...
Ignacio Guerrero's user avatar
  • 3
2 votes
1 answer
47 views

OK, the best way to describe this is with an example. (admittedly simplified) I want to predict the speed of drivers on a motorway and I have two input variables the nationality of the driver how ...
Jay Ramsay's user avatar
  • 23
0 votes
0 answers
65 views

I have a dataset of customer transactions containing revenue, customer id, region, product category, product id, support team, date of transaction etc. The data ranges from Jan 2017 to Nov 2nd 2022. ...
The Great's user avatar
  • 2,775
0 votes
1 answer
4k views

I am trying to use Linear Regression, to predict salary in USD. I have the following data: Data: 607 records Numerical columns: year, salary, salary in USD Categorical columns: experience, type, ...
Alix Blaine's user avatar
  • 125
0 votes
1 answer
44 views

I have fit.all <-lm(Sepal.Length ~ .,iris) summary(fit.all)->fit.all.summary print(fit.all.summary$coefficients) What are the coefficients ordered by?
Kirsten's user avatar
  • 67
0 votes
2 answers
66 views

I'm a newbie to tensorflow / keras and I am currently working my way through Deep Learning with Python (2nd edition) by Francois Chollet. I understand the basics of Computer vision and the MNIST ...
TF Newby's user avatar
  • 3
0 votes
0 answers
38 views

Let's say we are performing a linear regression, with general model $y(x,w) = w_0 + w_1x$. The error function is $E(w) = \frac{1}{2N}\sum_n ((y(x_n,w)-t_n)^2$, for $N$ datapoints ${(x_n,t_n)}$ (...
puradrogasincortar's user avatar
  • 123
0 votes
1 answer
788 views

I always use Linearregression() class in sklearn library for creating a linear regression model. According to my understanding, we need feature scaling in linear ...
AAA's user avatar
  • 45
3 votes
1 answer
1k views

The dummy variable trap is a common problem with linear regression when dealing with categorical variables, since one hot encoding introduces redundancy, so if we have m categories in our categorical ...
AAA's user avatar
  • 45
-1 votes
1 answer
61 views

I have been looking at how to calculate coefficients by hand and the example produces $Y = 1,383.471380 + 10.62219546 * X$ However the output shown of lm does not show these values anywhere. How do I ...
Kirsten's user avatar
  • 67
2 votes
1 answer
3k views

I was doing the modeling on the House Pricing dataset. My target is to get the mse result and predict with the input variable I have done the modeling, I'm doing the modeling with scaling the data ...
MADFROST's user avatar
  • 123

1
2
3 4 5
…
16