Databricks-Certified-Professional-Data-Scientist Exam Dumps : Databricks Certified Professional Data Scientist Exam

Since 10 out of 100 students are both French and female, then

P(AandB)=10100

Also. 60 out of the 100 students are French, so

P(B)=60100

So the required probability is:

P(A|B)=P(AandB)P(B)=10/10060/100=16

Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is just that L2 is the sum of the square of the weights, while L1 is just the sum of the weights. As follows: L1 regularization on least squares:

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The MAE measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables. The equation is given in the library references. Expressed in words, the MAE is the average over the verification sample of the absolute values of the differences between forecast and the corresponding observation. The MAE is a linear score which means that all the individual differences are weighted equally in the average.

The sum of absolute errors is a valid metric, but doesn't give any useful sense of how the recommender system is performing.

Support vector count and cluster density do not apply to recommender systems.

MAE and AUC are both valid and useful metrics for measuring recommender systems.