The model fit of my SGDRegressor wont increase or decrease its performance on the validation set (test) after around 20’000 training records. Even if I try to switch penalty, early_stopping (True/False) or alpha,eta0 to extremely high or low levels, there is no change in the behavior of the “stuck” validation score test. I used StandardScaler and shuffled the data for
Tag: linear-regression
Does it make sense? If yes then how to handle in MSE?
Can we do log transform to one variable and sqrt to another for LinearRegression? If yes then what to do during MSE? Should I exp or square the y_test and prediction? Answer If you transform variables in training and test sets you don’t need to care about your evaluation metric. In case you transform your target variable (with the log
How to label the line from transform_regression using Altair?
The code below creates a regression line; however, the legend defaults to labeling the line as “undefined.” How can this regression line be labeled in the legend as “reg-line”? Answer Simply add .transform_fold([“reg-line”], as_=[“Regression”, “y”]).encode(alt.Color(“Regression:N”)) after mark line Code should look like
Python: Develope Multiple Linear Regression Model From Scrath
I am trying to create a multiple linear regression model from scratch in python. Dataset used: Boston Housing Dataset from Sklearn. Since my focus was on the model building I did not perform any pre-processing steps on the data. However, I used an OLS model to calculate p-values and dropped 3 features from the data. After that, I used a
Unable to fix “ValueError: DataFrame constructor not properly called!”
I was asked to write a program for Linear Regression with the following steps. Load the R data set mtcars as a pandas dataframe. Build another linear regression model by considering the log of independent variable wt, and log of dependent variable mpg. Fit the model with data, and display the R-squared value I am a beginner at Statistics with
In the LinearRegression method in sklearn, what exactly is the fit_intercept parameter doing? [closed]
Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers. This question does not appear to be about programming within the scope defined in the help center. Closed 2 years ago. Improve this question In the sklearn.linear_model.LinearRegression method, there is a parameter that is fit_intercept = TRUE or fit_intercept = FALSE. I am wondering if
Missing observations and clustered standard errors in Python statsmodels?
What’s the cleanest, most pythonic way to run a regression only on non-missing data and use clustered standard errors? Imagine I have a Pandas dataframe all_data. Clunky method that works (make a dataframe without missing data): I can make a new dataframe without the missing data, make the model, and fit the model: This feels a bit clunky (esp. when
How to do linear regression, taking errorbars into account?
I am doing a computer simulation for some physical system of finite size, and after this I am doing extrapolation to the infinity (Thermodynamic limit). Some theory says that data should scale linearly with system size, so I am doing linear regression. The data I have is noisy, but for each data point I can estimate errorbars. So, for example