An apparatus and method are provided for dataset model fitting, and using a classifying engine to identify a statistical distribution for the dataset. The dataset classifying engine is configured to calculate a characterization function that represents a dataset and compute a feature vector for the dataset, where the feature vector encodes slope value changes corresponding to the characterization function. The dataset classifying engine receives a classification model and applies the classification model to the feature vector to identify a statistical distribution for the dataset.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A classifying engine, comprising: a non-transitory memory storage comprising instructions; and one or more processors in communication with the non-transitory memory storage, wherein the one or more processors execute the instructions to: calculate a characterization function that represents a dataset; compute a feature vector for the dataset, wherein the feature vector encodes slope value changes corresponding to the characterization function; receive a classification model; and apply the classification model to the feature vector to identify a statistical distribution for the dataset. 2. The classifying engine of claim 1, wherein the characterization function is a complementary cumulative distribution function (CCDF). 3. The classifying engine of claim 2, wherein, the one or more processors execute the instructions to identify points on a plot of the characterization function. 4. The classifying engine of claim 3, wherein a log scale is used on an x-axis of the plot and a log scale is used on a y-axis of the plot. 5. The classifying engine of claim 3, wherein the one or more processors execute the instructions to compute the feature vector by: discarding a first point and a last point of the points; computing a slope value for each pair of adjacent points that form a segment; and computing a difference between each pair of slope values to produce the slope value changes. 6. The classifying engine of claim 1, wherein the statistical distribution is a power-law distribution, a lognormal distribution, or a double-pareto lognormal distribution. 7. The classifying engine of claim 1, wherein the one or more processors execute the instructions to estimate parameters for the statistical distribution. 8. The classifying engine of claim 6, wherein the one or more processors execute the instructions to evaluate a residual sum of squares between the statistical distribution and the dataset. 9. The classifying engine of claim 1, wherein the classification model is trained using at least one of a power-law distribution, a lognormal distribution, or a double-pareto lognormal distribution. 10. The classifying engine of claim 1, wherein the classification model is trained using randomly generated training datasets and wherein, during training of the classification model, a training feature vector is computed for each training dataset. 11. The classifying engine of claim 1, wherein the classification model is trained by: calculating a training characterization function that represents a training dataset; and computing a training feature vector for the training dataset, wherein the feature vector encodes slope value changes corresponding to the training characterization function. 12. The classifying engine of claim 11, wherein the classification model is further trained by: identifying points on a plot of the training characterization function; discarding a first point and a last point of the points; computing a training slope value for each pair of adjacent points that form a segment; and computing the training feature vector for the training dataset based on the points, wherein the training feature vector encodes training slope value changes. 13. A method, comprising: receiving a dataset, by a classifying engine; calculating, by the classifying engine, a characterization function that represents the dataset; computing, by the classifying engine, a feature vector for the dataset, wherein the feature vector encodes slope value changes corresponding to the characterization function; receiving a classification model, by the classifying engine; and applying the classification model to the feature vector, by the classifying engine, to identify a statistical distribution for the dataset. 14. The method of claim 13, wherein the characterization function is a complementary cumulative distribution function (CCDF). 15. The method of claim 14, wherein, the one or more processors execute the instructions to identify points on a plot of the characterization function. 16. The method of claim 15, wherein a log scale is used on an x-axis of the plot and a log scale is used on a y-axis of the plot. 17. The method of claim 15, wherein, the one or more processors execute the instructions to compute the feature vector by: discarding a first point and a last point of the points; computing a slope value for each pair of adjacent points that form a segment; and computing a difference between each pair of slope values to produce the slope value changes. 18. The method of claim 13, wherein the statistical distribution is a power-law distribution, a lognormal distribution, or a double-pareto lognormal distribution. 19. The method of claim 13, wherein the one or more processors execute the instructions to estimate parameters for the statistical distribution. 20. The method of claim 18, wherein the one or more processors execute the instructions to evaluate a residual sum of squares between the statistical distribution and the dataset. 21. The method of claim 13, wherein the classification model is trained using at least one of a power-law distribution, a lognormal distribution, or a double-pareto lognormal distribution. 22. The method of claim 13, wherein the classification model is trained using randomly generated training datasets and wherein, during training of the classification model, a training feature vector is computed for each training dataset. 23. The method of claim 13, wherein the classification model is trained by: calculating a training characterization function that represents a training dataset; and computing a training feature vector for the training dataset, wherein the feature vector encodes slope value changes corresponding to the training characterization function. 24. The method of claim 23, wherein the classification model is further trained by: identifying points on a plot of the training characterization function; discarding a first point and a last point of the points; computing a training slope value for each pair of adjacent points that form a segment; and computing the training feature vector for the training dataset based on the points, wherein the training feature vector encodes training slope value changes. 25. A non-transitory computer-readable media storing computer instructions, that when executed by one or more processors, cause the one or more processors to perform the steps of: receiving a dataset; calculating a characterization function that represents the dataset; computing a feature vector for the dataset, wherein the feature vector encodes slope value changes corresponding to the characterization function; receiving a classification model; and applying the classification model to the feature vector to identify a statistical distribution for the dataset.