Q.1. The formula for standard error in Machine Learning is:

SE = σ / √n

where:
SE = Standard Error
σ = standard deviation of the population
n = size of the sample

Q.2. Function Approximation is a major concept in Machine Learning. It is the process of finding a function that closely fits a given set of data points. The goal is to find the underlying function that generated the data points. This function can then be used to predict new data points. This is done by minimizing the difference between the predicted values and the actual values. This difference is often measured using a loss function, such as mean squared error.

Q.3. The formula for Mean Squared Error (MSE) is:

MSE = 1/n Σ(yi - ŷi)²

where:
n = total number of observations
yi = actual value
ŷi = predicted value

Q.4. The evaluation matrix used in both classification and regression problems are different. For classification problems, we often use accuracy, precision, recall, F1 score, ROC AUC score etc. For regression problems, we often use Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R² score etc.

Q.5. Classification and Regression are two types of supervised learning methods in Machine Learning. Classification is about predicting a label and Regression is about predicting a quantity.

Classification predicts the category or class of an object or sample. It is used when the output of the model is a categorical variable, such as "spam" or "not spam" for an email.

Regression predicts a continuous value. It is used when the output of the model is a real or continuous value, such as "salary" or "price".

Question

Q.1. The formula for standard error in Machine Learning is:

SE = σ / √n

where:
SE = Standard Error
σ = standard deviation of the population
n = size of the sample

Q.2. Function Approximation is a major concept in Machine Learning. It is the process of finding a function that closely fits a given set of data points. The goal is to find the underlying function that generated the data points. This function can then be used to predict new data points. This is done by minimizing the difference between the predicted values and the actual values. This difference is often measured using a loss function, such as mean squared error.

Q.3. The formula for Mean Squared Error (MSE) is:

MSE = 1/n Σ(yi - ŷi)²

where:
n = total number of observations
yi = actual value
ŷi = predicted value

Q.4. The evaluation matrix used in both classification and regression problems are different. For classification problems, we often use accuracy, precision, recall, F1 score, ROC AUC score etc. For regression problems, we often use Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R² score etc.

Q.5. Classification and Regression are two types of supervised learning methods in Machine Learning. Classification is about predicting a label and Regression is about predicting a quantity.

Classification predicts the category or class of an object or sample. It is used when the output of the model is a categorical variable, such as "spam" or "not spam" for an email.

Regression predicts a continuous value. It is used when the output of the model is a real or continuous value, such as "salary" or "price".

Knowee AI · Accepted Answer

Q.1. The formula for standard error in Machine Learning is:

SE = σ / √n

where:
SE = Standard Error
σ = standard deviation of the population
n = size of the sample

Q.2. Function Approximation is a major concept in Machine Learning. It is the process of finding a function that closely fits a given set of data points. The goal is to find the underlying function that generated the data points. This function can then be used to predict new data points. This is done by minimizing the difference between the predicted values and the actual values. This difference is often measured using a loss function, such as mean squared error.

Q.3. The formula for Mean Squared Error (MSE) is:

MSE = 1/n Σ(yi - ŷi)²

where:
n = total number of observations
yi = actual value
ŷi = predicted value

Q.4. The evaluation matrix used in both classification and regression problems are different. For classification problems, we often use accuracy, precision, recall, F1 score, ROC AUC score etc. For regression problems, we often use Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R² score etc.

Q.5. Classification and Regression are two types of supervised learning methods in Machine Learning. Classification is about predicting a label and Regression is about predicting a quantity.

Classification predicts the category or class of an object or sample. It is used when the output of the model is a categorical variable, such as "spam" or "not spam" for an email.

Regression predicts a continuous value. It is used when the output of the model is a real or continuous value, such as "salary" or "price".

Q.1. What Is the formula of standard error in ML.Q.2. Explain Function Approximation in Detrail.Q.3.What is the formula of MSE.Q.4.What is the evaluation matrix used in both the types of problems.Q.5. What is Classification and Regression.

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