User contributions
- 08:39, 12 December 2017 (diff | hist) . . (+164) . . TDSM 11.9 (current)
- 16:10, 11 December 2017 (diff | hist) . . (-2) . . TDSM 5.1
- 00:41, 11 December 2017 (diff | hist) . . (+57) . . TDSM 7.3
- 07:40, 29 November 2017 (diff | hist) . . (+2) . . TDSM 8.15 (current)
- 06:00, 29 November 2017 (diff | hist) . . (+3) . . TDSM 8.11
- 04:02, 29 November 2017 (diff | hist) . . (0) . . m TDSM 8.5 (current)
- 18:16, 26 November 2017 (diff | hist) . . (+423) . . N TDSM 7.13 (Created page with "The complete data is divided into training, test and validation data. Training Data : This data is used to train our model. Validation Data : This dataset is used to find ou...") (current)
- 18:13, 26 November 2017 (diff | hist) . . (+280) . . N TDSM 7.7 (Created page with "Precision is how many of our positive guesses are true out of all examples for which we said yes. <math>Precision = \frac{TP}{TP + FP}</math> Recall is how many of our posit...")
- 17:50, 26 November 2017 (diff | hist) . . (+655) . . N TDSM 7.9 (Created page with "When a model learns the noise instead of signal, it is said to be overfit. A method to check whether a model is overfit is that when our model works very well on training dat...")
- 16:38, 26 November 2017 (diff | hist) . . (+312) . . N TDSM 7.3 (Created page with "Examples: # First Principle Model : A model predicting the result of a football match. Result of a match depends on the form of its players. So features measuring the form of...")
- 16:04, 26 November 2017 (diff | hist) . . (+463) . . N TDSM 7.1 (Created page with "According to Occam's Razor principle, the simplest explanations are the best. In the question it is stated that Quantum physics is much more complicated than Newtonian physics...")
- 21:32, 23 November 2017 (diff | hist) . . (+728) . . TDSM 3.17
- 21:01, 23 November 2017 (diff | hist) . . (+912) . . N TDSM 3.15 (Created page with "'''Below are some of the ways to screen an outlier from dataset:''' * We can visualize the data by using graphs and find candidates for outliers. * We can analyze the data and...") (current)
