Started watching Week2 of Andrew Ng's coursera Machine Learning course.
Beginning with Model & Cost function. For Model representation, showed correlation of house size to sale price in Portland. In this example, the house size is the x-value input, and the y-value is the sale price. It is a supervised learning scenario, since we train the model on known historical sales prices.
This is an example of univariate linear regression, or linear regression with one variable. Because we're trying to predict continuous real values, we call this a "regression problem". Otherwise, if there are only discrete values, it is a "classification problem".
To validate this scenario for the financial projections case, I'll use the
Keys to pr
google_vix_results.csv data below, which attempts to predict the high of the GOOG equity, from the prior day's GOOGVIX volatility index high. The
prior_day_vix_futures_high will be the x and the the
observed_equity_high will be the y. m is