I have two vectors of data and I've put them into pyplot.scatter(). Now I'd like to over plot a linear fit to these data. How would I do this? I've tried using scikitlearn and np.polyfit().

I tried fitting an OLS for Boston data set. My graph looks like below. How to annotate the linear regression equation just above the line or somewhere in the graph? How do I print the equation in P...

I've come across somewhat of a confusing topic relating to the syntax of multiple regression with explanatory variables and their interactions. A DataCamp explanation led me to think that: lm(formu...

Using the following small dataset: bill = [34,108,64,88,99,51] tip = [5,17,11,8,14,5] I calculated a best-fit regression line (by hand). yi = 0.1462*x - 0.8188 #yi = slope(x) + intercept I've pl...

I am using Linear regression to predict data. But, I am getting totally contrasting results when I Normalize (Vs) Standardize variables. Normalization = x -xmin/ xmax – xmin   Zero Score Standard...

I calculated a model using OLS (multiple linear regression). I divided my data to train and test (half each), and then I would like to predict values for the 2nd half of the labels. model = OLS(la...

I'm trying to perform feature selection by evaluating my regressions coefficient outputs, and select the features with the highest magnitude coefficients. The problem is, I don't know how to get th...

Just like we use the Normal Equation to find out the optimum theta value in Linear Regression, can/can't we use a similar formula for Logistic Regression ? If not, why ? I'd be grateful if could so...

I've trained a Linear Regression model with R caret. I'm now trying to generate a confusion matrix and keep getting the following error: Error in confusionMatrix.default(pred, testing$Final) : th...

Is there a library in python to do segmented linear regression? I'd like to fit multiple lines to my data automatically to get something like this: Btw. I do know the number of segments.

I'm using linear_model.LinearRegression from scikit-learn as a predictive model. It works and it's perfect. I have a problem to evaluate the predicted results using the accuracy_score metric. This ...

I am working with sklearn and specifically the linear_model module. After fitting a simple linear as in import pandas as pd import numpy as np from sklearn import linear_model randn = np.random.ra...

I am trying to use Ordinary Least Squares for multivariable regression. But it says that there is no attribute 'OLS' from statsmodels. formula. api library. I am following the code from a lecture o...

When it comes to measuring goodness of fit - R-Squared seems to be a commonly understood (and accepted) measure for "simple" linear models. But in case of statsmodels (as well as other statistical ...

I can't seem to find any python libraries that do multiple regression. The only things I find only do simple regression. I need to regress my dependent variable (y) against several independent vari...

Is there a way to create a Seaborn line plot with all the lines gray and the mean as a red line? I'm trying to do this with relplot but I don't know how to separate the mean from the data (and it a...

My DataFrame object looks like amount date 2014-01-06 1 2014-01-07 1 2014-01-08 4 2014-01-09 1 2014-01-14 1 I would like a sort of scatter plot with time along the x-axis, and amount on the y, w...

I have seen similar posts like this one which say that getting the error message saying: Coefficients: (1 not defined because of singularities) is because of nearly perfect correlation among predic...

I am currently running multiple linear regression on a dataset. At first, I didn't realize I needed to put constraints over my weights; as a matter of fact, I need to have specific positive & n...

I'm using the felm() function from the lfe package to fit linear models with large numbers of fixed effects. I would like to be able to fit a model using only fixed effects. For instance, I'd like ...

I'm trying to find a way to fit a linear regression model with positive coefficients. The only way I found is sklearn's Lasso model, which has a positive=True argument, but doesn't recommend using ...

I have a sample time-series dataframe: df = pd.DataFrame({'year':'1990','1991','1992','1993','1994','1995','1996', '1997','1998','1999','2000'], 'count':[96,184,148,154,160,149,124,274,322,301,30...

I've been trying to get the standard error & p-Values by using LR from scikit-learn. But no success. I've end up finding up this article: but the std error & p-value does not match that f...

I am performing component wise regression on a time series data. This is basically where instead of regressing y against x1, x2, ..., xN, we would regress y against x1 only, y against x2 only, ...,...

Given the following: import numpy as np import pandas as pd import seaborn as sns np.random.seed(365) x1 = np.random.randn(50) y1 = np.random.randn(50) * 100 x2 = np.random.randn(50) y2 = np.rando...