Yes, you will have to convert everything to numbers. That requires thinking about what these attributes represent. Usually there are three possibilities: One-Hot encoding for categorical data Arbitrary numbers for ordinal data Use something like group means for categorical data (e. g. mean prices for city districts). You have to be carefull to not infuse … Read more
When you use the normal equations for solving the cost function analytically you have to compute: Where X is your matrix of input observations and y your output vector. The problem with this operation is the time complexity of calculating the inverse of a nxn matrix which is O(n^3) and as n increases it can … Read more
import numpy as np from numpy.polynomial.polynomial import polyfit import matplotlib.pyplot as plt # Sample data x = np.arange(10) y = 5 * x + 10 # Fit with polyfit b, m = polyfit(x, y, 1) plt.plot(x, y, ‘.’) plt.plot(x, b + m * x, ‘-‘) plt.show()
Despite the plethora of wrong answers here that attempt to circumvent the error by numerically manipulating the predictions, the root cause of your error is a theoretical and not computational issue: you are trying to use a classification metric (accuracy) in a regression (i.e. numeric prediction) model (LinearRegression), which is meaningless. Just like the majority … Read more
arange generates lists (well, numpy arrays); type help(np.arange) for the details. You don’t need to call it on existing lists. >>> x = [1,2,3,4] >>> y = [3,5,7,9] >>> >>> m,b = np.polyfit(x, y, 1) >>> m 2.0000000000000009 >>> b 0.99999999999999833 I should add that I tend to use poly1d here rather than write out … Read more
Since 2009, dplyr has been released which actually provides a very nice way to do this kind of grouping, closely resembling what SAS does. library(dplyr) d <- data.frame(state=rep(c(‘NY’, ‘CA’), c(10, 10)), year=rep(1:10, 2), response=c(rnorm(10), rnorm(10))) fitted_models = d %>% group_by(state) %>% do(model = lm(response ~ year, data = .)) # Source: local data frame [2 … Read more
See the relevel() function. Here is an example: set.seed(123) x <- rnorm(100) DF <- data.frame(x = x, y = 4 + (1.5*x) + rnorm(100, sd = 2), b = gl(5, 20)) head(DF) str(DF) m1 <- lm(y ~ x + b, data = DF) summary(m1) Now alter the factor b in DF by use of the … Read more
sklearn.linear_model.LinearRegression will do it: from sklearn import linear_model clf = linear_model.LinearRegression() clf.fit([[getattr(t, ‘x%d’ % i) for i in range(1, 8)] for t in texts], [t.y for t in texts]) Then clf.coef_ will have the regression coefficients. sklearn.linear_model also has similar interfaces to do various kinds of regularizations on the regression.
In general, to provide your own formula you should use arguments x and y that will correspond to values you provided in ggplot() – in this case x will be interpreted as x.plot and y as y.plot. You can find more information about smoothing methods and formula via the help page of function stat_smooth() as … Read more
For fitting y = A + B log x, just fit y against (log x). >>> x = numpy.array([1, 7, 20, 50, 79]) >>> y = numpy.array([10, 19, 30, 35, 51]) >>> numpy.polyfit(numpy.log(x), y, 1) array([ 8.46295607, 6.61867463]) # y ≈ 8.46 log(x) + 6.62 For fitting y = AeBx, take the logarithm of both … Read more