In a linear regression problem, h(x) is the predicted value of the target variable, y is the actual value of the target variable, m is the number of training examples. What do we try to minimize?

In a linear regression problem, h(x) is the predicted value of the target variable, y is the actual value of the target variable, m is the number of training examples. What do we try to minimize? Correct Answer (h(x) – y)2 / 2*m

The objective is to find the difference between the predicted value and actual value of the target variable and to minimize this error. If we get a negative value, then no minimizing can be done, that’s why squaring is done. To get an average over the dataset, the squared value is divided by twice the number of training examples.
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We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. Now we increase the training set size gradually. As the training set size increases, what do you expect will happen with the mean training error?
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2. When lambda is 0, model doesn't work like linear regression model
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1. Opportunities
2. Training executed
3. Post training developments
4. Skill gaps
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6. Number retained in other new trades/skill
The difference between the actual Y value and the predicted Y value found using a regression equation is called the
Statement I The product of coefficient of correlation, standard deviation of variable 'X' and Standard deviation of variable 'Y' gives the measure of covariance between X and Y variables.
Statement II The product of coefficient of correlation between X and Y variables and the ratio between standard deviation of X variable to standard deviation of Y variable measures the slope of the regression line of X on Y variable.
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1. Linear Regression
2. Logistic Regression
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Reason (R): The geometric mean between two regression coefficients is the coefficient of correlation.
Assertion (A): If regression coefficient of X on Y is greater than one, regression coefficient of Y on X must be less than one.
Reason (R): The geometric mean between two regression coefficients is the co-efficient of correlation.
On the basis of the above, choose the appropriate answer :