How to setup a Regression Experiment using Boston Housing dataset in Keras
Setting up a regression experiment using the Boston Housing dataset in Keras involves several steps. First, you need to import the Boston Housing dataset, which contains information about the median value of homes in the Boston area, and various features of the neighborhood such as the crime rate, the number of rooms, and the age of the houses. The dataset contains 506 samples for training and 102 samples for testing.
Next, you will need to preprocess the data. This includes normalizing the data to ensure that all the features are on the same scale, and splitting the data into training and testing sets.
After that, you will need to define the model architecture. The architecture of the model is the structure of the layers and the number of units or neurons in each layer. This can be done using the Sequential class in Keras and adding layers to it. The architecture should be appropriate for the specific task of regression.
After that, you will need to choose the optimizer and the learning rate. The optimizer is used to adjust the weights of the model to minimize the loss function, and the learning rate controls the step size that the optimizer takes in the direction of the gradient.
You will also need to decide the evaluation metrics that you will use to evaluate the model performance. The most common evaluation metrics for regression models include mean squared error, mean absolute error and R-squared.
Finally, you will need to decide the number of training iterations (epochs) and the batch size. The number of epochs controls the number of times the model will see the entire dataset during training, while the batch size controls the number of samples that the model sees at a time.
In summary, setting up a regression experiment using the Boston Housing dataset in Keras involves importing the dataset, preprocessing the data, defining the model architecture, choosing the optimizer and learning rate, deciding the evaluation metrics, and deciding the number of training iterations and batch size. The goal of this experiment is to train a deep learning model that is able to predict the median value of homes in the Boston area based on various features of the neighborhood with a high level of accuracy.
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