Databricks Databricks-Certified-Professional-Data-Scientist Dumps Questions Answers

If you re trying to predict or forecast a target value, then you need to look into supervised learning. If not, then unsupervised learning is the place you want to be. If you've chosen supervised learning, what's your target value? Is it a discrete value like Yes/No, 1/2/3, A/B/C: or Red/Yellow/Black? If so, then you want to look into classification. If the target value can take on a number of values, say any value from 0.00 to 100.00, or-999 to 999, or+_to -_, then you need to look into regression. If you're not trying to predict a target value: then you need to look into unsupervised learning. Are you trying to fit your data into some discrete groups? If so and that's all you need, you should look into clustering. Do you need to have some numerical estimate of how strong the fit is into each group? If you answer yes then you probably should look into a density estimation algorithm.

There is a tradeoff between cost and stability in unsupervised learning. The more tightly you fit the data, the less stable the model will be, and vice versa. The idea is to find a good balance with more weight given to the cost. Typically a good approach is to set a stability threshold and select the model that achieves the lowest cost above the stability threshold.

Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.

The advantages of support vector machines are:

Effective in high dimensional spaces.

Still effective in cases where number of dimensions is greater than the number of

samples.

Uses a subset of training points in the decision function (called support vectors), so

it is also memory efficient.

Versatile: different Kernel functions can be specified for the decision function.

Common kernels are provided, but it is also possible to specify custom kernels.

The disadvantages of support vector machines include:

If the number of features is much greater than the number of samples, the method

is likely to give poor performances.

SVMs do not directly provide probability estimates, these are calculated using an

expensive five-fold cross-validation.