linear-regression

I have a dataset including categorical variables(binary) and continuous variables. I'm trying to apply a linear regression model for predicting a continuous variable. Can someone please let me know how to check for correlation among the categorical variables and the continuous target variable. Current Code: import pandas as pd df_hosp = pd.read_csv('C:\Users\LAPPY-2\Desktop\LengthOfStay.csv') data = df_hosp[['lengthofstay', 'male', 'female', 'dialysisrenalendstage', 'asthma', \ 'irondef', 'pneum', 'substancedependence', \ 'psychologicaldisordermajor', 'depress', 'psychother', \

I'm working on machine learning problem and want to use linear regression as learning algorithm. I have implemented 2 different methods to find parameters theta of linear regression model: Gradient (steepest) descent and Normal equation. On the same data they should both give approximately equal theta vector. However they do not. Both theta vectors are very similar on all elements but the first one. That is the one used to multiply vector of all 1 added to the data. Here is how the theta s look like (fist column is output of Gradient descent, second output of Normal equation): Grad desc Norm

How does plot.lm() determine what points are outliers (that is, what points to label) for residual vs fitted plot? The only thing I found in the documentation is this: Details sub.caption—by default the function call—is shown as a subtitle (under the x-axis title) on each plot when plots are on separate pages, or as a subtitle in the outer margin (if any) when there are multiple plots per page. The ‘Scale-Location’ plot, also called ‘Spread-Location’ or ‘S-L’ plot, takes the square root of the absolute residuals in order to diminish skewness (sqrt(|E|)) is much less skewed than | E | for

So, i'm trying to understand how the SVM algorithm works but i just cannot figure out how you transform some datasets in points of n-dimensional plane that would have a mathematical meaning in order to separate the points through a hyperplane and clasify them. There's an example here , they are trying to clasify pictures of tigers and elephants, they say "We digitize them into 100x100 pixel images, so we have x in n-dimensional plane, where n=10,000", but my question is how do they transform the matrices that actually represent just some color codes IN points that have a methematical meaning

In the sklearn.linear_model.LinearRegression method, there is a parameter that is fit_intercept = TRUE or fit_intercept = FALSE . I am wondering if we set it to TRUE, does it add an additional intercept column of all 1's to your dataset? If I already have a dataset with a column of 1's, does fit_intercept = FALSE account for that or does it force it to fit a zero intercept model? Update: It seems people do not get my question. The question is basically, what IF I had already a column of 1's in my dataset of predictors (the 1's are for the intercept). THEN, 1) if I use fit_intercept = FALSE ,

I'm trying to create a 3d plot of a linear model fit for a data set. I was able to do this relatively easily in R, but I'm really struggling to do the same in Python. Here is what I've done in R: Here's what I've done in Python: from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt import numpy as np import pandas as pd import statsmodels.formula.api as sm csv = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0) model = sm.ols(formula='Sales ~ TV + Radio', data = csv) fit = model.fit() fit.summary() fig = plt.figure() ax = fig.add_subplot(111,

I am performing a least squares regression as below (univariate). I would like to express the significance of the result in terms of R^2. Numpy returns a value of unscaled residual, what would be a sensible way of normalizing this. field_clean,back_clean = rid_zeros(backscatter,field_data) num_vals = len(field_clean) x = field_clean[:,row:row+1] y = 10*log10(back_clean) A = hstack([x, ones((num_vals,1))]) soln = lstsq(A, y ) m, c = soln [0] residues = soln [1] print residues See http://en.wikipedia.org/wiki/Coefficient_of_determination Your R2 value = 1 - residual / sum((y - y.mean())**2)