Learn forecasting here! We'll go through some basic concepts.

• Predict a y value for every x. y-hat is the predicted value.
• Simple linear model with slope and intercept.

• Linear regression is a model creation procedure:
  • Use J, the cost function, to measure which models are better (with less error):

• J is arbitrary, but the one listed above is a rough overview of least-squares error. It is also referred to as Sum of Squared Error (SSE).

• There are different ways of optimizing H(x) using J, including gradient descent, which involves using the gradient of J to find a point where all parts of the gradient are 0.
• When the gradient is 0, the function given has the least amount of error.
  • Catch: just because gradient = 0 doesn't mean that it's a global minimum!
• We can solve for the zero-point of the gradient using calculus and the partial derivative of J with respect to x or each of x1, x2, x3, etc.. if there is more than one feature you use to find y.

• Use the cost function, J, to optimize, H, the prediction model.
• Here, H is a linear function: H(x) = mx + b.
• J is often SSE/least square error.
• Apply linear algebra when the data grows to a larger amount of input features: Y = AX + B.

• Split a group of data into K groups of equal size.
• Use a certain amount of groups as the training, then use the rest as test data.
• Use all combinations of training/test data across the folds to get multiple models.
• Aggregate the models somehow to get a final model.

• Split the data between training and test, get models, aggregate models.

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