Читать книгу Machine Learning For Dummies - John Paul Mueller, John Mueller Paul, Luca Massaron - Страница 17
Considering the goals of machine learning
ОглавлениеAt present, AI is based on machine learning, and machine learning is essentially different from statistics. Yes, machine learning has a statistical basis, but it makes some different assumptions than statistics do because the goals are different. Table 1-1 lists some features to consider when comparing AI and machine learning to statistics.
TABLE 1-1: Comparing Machine Learning to Statistics
| Technique | Machine Learning | Statistics |
|---|---|---|
| Data handling | Works with big data in the form of networks and graphs; raw data from sensors or the web text is split into training and test data. | Models are used to create predictive power on small samples. |
| Data input | The data is sampled, randomized, and transformed to maximize accuracy scoring in the prediction of out-of-sample (or completely new) examples. | Parameters interpret real-world phenomena and provide a stress on magnitude. |
| Result | Probability is taken into account for comparing what could be the best guess or decision. | The output captures the variability and uncertainty of parameters. |
| Assumptions | The scientist learns from the data. | The scientist assumes a certain output and tries to prove it. |
| Distribution | The distribution is unknown or ignored before learning from data. | The scientist assumes a well-defined distribution. |
| Fitting | The scientist creates a best fit, but generalizable, model. | The result is fit to the present data distribution. |
