求殘差值平方能使計算以「絕對」值 (忽略差數是否為正數或負數) 為基礎,並為較大的差數提供更多權數。 此計量稱為平均平方誤差(MSE)。 針對我們的驗證資料,計算如下列範例所示: 展開資料表 因此,以 MSE 計量為基礎的模型損失為 9.79。 這代表好或壞呢? 很難分辨,因為 MSE 值未以有意義的測量單位表示。 我...
If the coefficient of determination (R-squared) in a regression of Y on X is 0.930, what is the unexplained variation in a regression of Y on X? In regression forecasting, what do we mean when we say that there is linearity in a set of data? Explain how regression analysis ...
Finding the optimal lambda value is crucial for achieving a good trade-off between bias and variance in ridge regression models. How Ridge Regression Works? Ridge regression is a linear regression technique used to handle the problem of multicollinearity, where predictor variables in a dataset are ...
Now that we have a good understanding of a basic confusion matrix, its terminology, and its use, let’s move on to manually calculating a confusion matrix, followed by a practical example. Manually calculating a confusion matrix Here is a step-by-step guide on how to manually calculate a ...
2. What is Mean Squared Error or MSE The Mean Absolute Error is the squared mean of the difference between the actual values and predictable values. How do you Calculate MSE? Steps to calculate the MSE from a set of X and Y values: First, Find the regression line. Insert...
ANOVA What is the critical value for A effect? What is the appropriate Alternative Hypothesis for ANOVA? What are the assumptions we must satisfy when conducting a two-way, between-subjects ANOVA? Will you have three answers as a result when completing a Two-Way ANOVA?
one variable is called an independent variable, and the other is a dependent variable. Linear regression is commonly used for predictive analysis. The main idea of regression is to examine two things. First, does a set of predictor variables do a good job in predicting an outcome (dependent)...
The first advantage of deep learning over machine learning is the redundancy of the so-called feature extraction.Long before we began using deep learning, we relied on traditional machine learning methods including decision trees, SVM, naïve Bayes classifier and logistic regression. These algorithms...
like image recognition or classification, we’ll leverage supervised learning, or labeled datasets, to train the algorithm. As we train the model, we’ll want to evaluate its accuracy using a cost (or loss) function. This is also commonly referred to as the mean squared error (MSE). In ...
a. A b. B c. x d. R e. R^2 Regression model: The regression model is the mathematical linear relationship between a variable and one or more variables. The model has a slope coefficient which represents the change in one variable with respect to another. There ...