馃摎 The goal is to find a function f-hat that can make predictions y hat from the data.
馃搱 We want to find the best fit line (F-hat) that minimizes the errors between predicted and actual labels.
馃挕 Two options to find the best fit line are parametric and non-parametric methods.
馃搹 Parametric methods involve making assumptions about the shape or form of the data.
馃攳 Nonparametric methods do not make assumptions about the shape or form of the data.
馃搱 Estimating the function is simplified when assuming a linear form.
馃搳 Parametric methods simplify the estimation problem by using known parameters.
馃攢 Nonparametric methods do not assume a specific form for the data distribution.
馃攧 Parametric models may not accurately match the true form of the data.
Choosing a linear model for non-linear data results in poor predictions.
Using a parametric method involves expressing output based on a linear equation.
Estimating the beta values allows for predicting sales in the advertising dataset.
馃攽 Parametric methods use predetermined parameters to make predictions based on specific assumptions.
馃攽 Nonparametric methods do not make explicit assumptions about the shape or form of the data and seek to estimate it based on the data points.
馃攽 Nonparametric methods have the advantage of being able to accurately fit a wider range of possible functions.
馃搳 Parametric methods assume a certain shape for the data, which can result in inaccurate predictions if the actual data shape is different.
馃攧 Nonparametric methods do not make any assumptions about the shape of the data, resulting in better fitting models.
馃搲 However, nonparametric methods require a larger amount of data for accurate estimation.
馃搳 Parametric methods rely on predefined assumptions about the data, while nonparametric methods do not.
馃搲 Nonparametric methods may struggle to accurately estimate the response for observations not included in the training dataset.
馃攳 Having a large, clean dataset with minimal outliers and noise is crucial for accurately estimating the best line in nonparametric methods.
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