numerical samples
estimates values
MMR
Transforms features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, i.e. between zero and one.
array of integers or floats
This function returns two functions that can mix max scale new inputs and reverse scale new outputs
Standardize features by removing the mean and scaling to unit variance
Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Mean and standard deviation are then stored to be used on later data using the transform method.
Standardization of a dataset is a common requirement for many machine learning estimators: they might behave badly if the individual feature do not more or less look like standard normally distributed data (e.g. Gaussian with 0 mean and unit variance)
array of integers or floats
This function returns two functions that can standard scale new inputs and reverse scale new outputs
You can use the adjusted coefficient of determination to determine how well a multiple regression equation “fits” the sample data. The adjusted coefficient of determination is closely related to the coefficient of determination (also known as R2) that you use to test the results of a simple regression equation.
the number of independent variables in the regression equation
adjusted r^2 for multiple linear regression
Converts z-score into the probability
Number of standard deviations from the mean.
p - p-value
The coefficent of Correlation is given by R decides how well the given data fits a line or a curve.
numerical samples
estimates values
R
In statistics, the coefficient of determination, denoted R2 or r2 and pronounced "R squared", is the proportion of the variance in the dependent variable that is predictable from the independent variable(s). Compares distance of estimated values to the mean. {\bar {y}}={\frac {1}{n}}\sum {i=1}^{n}y{i}
numerical samples
estimates values
r^2
The errors (residuals) from acutals and estimates
numerical samples
estimates values
errors (residuals)
returns a safe column name / url slug from a string
Mean Absolute Deviation (MAD) indicates the absolute size of the errors
numerical samples
estimates values
MAD
MAPE (Mean Absolute Percent Error) measures the size of the error in percentage terms
numerical samples
estimates values
MAPE
The bias of forecast accuracy
numerical samples
estimates values
MFE (bias)
The standard error of the estimate is a measure of the accuracy of predictions made with a regression line. Compares the estimate to the actual value
numerical samples
estimates values
MSE
The coefficent of determination is given by r^2 decides how well the given data fits a line or a curve.
r^2
Returns an array of the squared different of two arrays
Squared difference of left minus right array
The standard error of the estimate is a measure of the accuracy of predictions made with a regression line. Compares the estimate to the actual value
numerical samples
estimates values
Standard Error of the Estimate
Calculates the z score of each value in the sample, relative to the sample mean and standard deviation.
An array like object containing the sample data.
The z-scores, standardized by mean and standard deviation of input array
Tracking Signal - Used to pinpoint forecasting models that need adjustment
numerical samples
estimates values
trackingSignal
MAD over Mean Ratio - The MAD/Mean ratio is an alternative to the MAPE that is better suited to intermittent and low-volume data. As stated previously, percentage errors cannot be calculated when the actual equals zero and can take on extreme values when dealing with low-volume data. These issues become magnified when you start to average MAPEs over multiple time series. The MAD/Mean ratio tries to overcome this problem by dividing the MAD by the Mean—essentially rescaling the error to make it comparable across time series of varying scales
util
https://www.forecastpro.com/Trends/forecasting101August2011.html
const actuals = [ 45, 38, 43, 39 ]; const estimates = [ 41, 43, 41, 42 ]; const MMR = ms.util.MADMeanRatio(actuals, estimates); MAPE.toFixed(2) // => 0.08