![]() Compare this with the fitted equation for the ordinary least squares model: Progeny 0.12703 + 0.2100 Parent. The resulting fitted equation from Minitab for this model is: Progeny 0.12796 + 0.2048 Parent. The only caveat is that my real-world data doesn't always imply the solution is a monotonically increasing function, but my ideal solution will be. In other words, we should use weighted least squares with weights equal to 1 / S D 2. If a solution using func and curve_fit is possible I'm open to that too, or any other methods. Consider unit 3765, which has a weight of 6. Plt.plot(X, func(X, *popt), color = 'green') The general formula seems to be size of possible match set/size of actual match set, and summed for every treated unit to which a control unit is matched. Popt, pcov = curve_fit(func, X, Y, bounds=(, )) Other than using this approach I've also used curve_fit to use a power function or exponential function: from scipy.optimize import curve_fit If I want to use this model to predict the future, the non-weighted models will always be too conservative in their prediction as they won't be as sensitive to the newest data. ![]() James Phillips at 17:36 Add a comment 1 Answer Sorted by: 3 There are plenty of sites to help with this sort of question, but I will highlight a few. This weighted model would have a similar curve but would fit the newer points better. weight (Tensor, optional) a manual rescaling weight given to the loss of each batch element. 1 Weights are often used to account for uncertainty in the data values, for example if a given data point's value is very uncertain then the weight for that data point is small. The resulting fitted equation from Minitab for this model is: Progeny 0.12796 + 0.2048 Parent Compare this with the fitted equation for the ordinary least squares model: Progeny 0.12703 + 0. I'm wondering if the sklearn package (or any other python packages) has this feature? In other words, we should use weighted least squares with weights equal to 1 / S D 2. Objective: To develop regression models that can predict corrected height, weight and obesity prevalence from self-reported data, as well as to compare obesity. There is a question about doing this in R: I'd like to adjust my model such that the newest data points are weighted the highest. I believe the newest data points are the most important as they are the most recent and most indicative of future behavior. A more complex, multi-variable linear equation might look like this, where w represents the coefficients, or weights, our model will try to learn. So we're modeling some behavior over time. Now imagine that the X values are time-based and the Y value is a snapshot of a sensor. ![]() Plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)), color='blue') If we look at a simple example: import matplotlib.pyplot as pltįrom sklearn.preprocessing import PolynomialFeatures, normalizeįrom sklearn.linear_model import LinearRegression I'd like to add weights to my training data based on its recency.
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