Noise - Noise introduces random error and variability that makes it harder to see the true relationship, Underfit - When a model does not capture the true relationship, ignoring key data, Overfit - When the model is oversensitive to noise and outliers, Exponential - y= abx, Linear - y = mx+b, Quadratic - y = ax2+bx+c, Logarithmic - Used when rate increases rapidly at first and then declines over time, Logistic - Used to model binary options, such as Yes or No, Response - Dependent, or y variable, Explanatory - Independent, or x variable, Confusion Matrix - Evaluates whether a classification model correctly identifies data, Probability - When using a logistic curve, it calculates this, Correlation - r, Coefficient of Determination - r2, MSE - The average of the errors, squared, RMSE - The average of the errors, MAE - The average of the absolute differences between predicted and actual values, Interpolation - A prediction made when using a value within the data, Extrapolation - A prediction made when using a value outside the data, Residual - The difference between the observed and predicted value,

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