The whole idea behind deep learning is to have the computers artificially mimic biological natural intelligence, so let’s build a general understanding of how biological neurons works.
Whatever we do, we need evaluation to let us know right or wrong.
I believe you all know what is Regression task. For example
The most common evaluation metrics for regression:
→ Mean Absolute Error (MAE).
→ Mean Squared Error (MSE).
→ Root Mean Squared Error (RMSE).
Linear Regression works for continuous variable prediction. EX: Prediction of percentage mark scored by the student.
Logistic Regression works for discrete variable prediction. EX: Predicting Pass or Fail of the Student.
Why do we need regularization methods? Answer: If the Linear Regression model leads to overfitting, we apply regularization techniques to avoid overfitting issue.
Before getting into regularization techniques, we can understand about overfitting and underfitting.
Bias - Error between average model predictions and ground truth.
Variance -Average variability in the model prediction for the given dataset.
Linear Regression is the first step to climb the ladder of machine learning algorithm. Linear Regression comes under supervised learning where we have to train the Linear Regression model to predict data.
The Goal is to find the best fit line by minimizing the vertical distance, i.e. The error between predicted and actual value.
Linear regression is used for finding linear relationship between target and one or more predictors. …
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