Classification vs Regression

Classification:

Classification algorithms attempt to predict a discrete class label. For example, a problem where you are trying to build a model(or algorithm) to interpret whether an image is a dog or a cat would be a classification problem.

Some Common Classification Machine Learning Algorithms/models:
- K Nearest Neighbors
- Support Vector Machine
- Logistic Regression
- Random Forest Classifier
- Decision Trees
- Neural Networks

Regression:

On the other hand, regression algorithms attempt to predict a continuous quantity. An example of a regression problem would be when you're trying to predict the prices of homes, where the prediction could be an integer, fraction, or decimal number.

Some Common Regression Machine Learning Algorithms/models:
- Linear Regressor
- Polynomial Regressor
- Lasso Regressor
- Ridge Regressor
- Elastic Net Regressor
- Support Vector Regressor
- Regression Trees
- Neural Networks

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Overfitting vs Underfitting

Overfitting vs Underfitting Hello! In this post, we will be exploring the concepts of Overfitting and Underfitting. Overfitting: Overfitting is a modelling error that occurs when the model fits the training data too well. As you can see above, the overfitted model is fit almost perfectly to the training data. The overall cost of the model to the training data be near 0, however, the accuracy of this model would be poor when used on testing data. Underfitting: Underfitting is when the model fits to the training data too simply; when the model isn't complex enough to adequately understand the trend/pattern of the training data. As you can see from the image above, the model is just a linear line and does not fit to the training data very well; the model does not accurately understand the trend of the data and is fit too simply.

Linear Regression

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