What are the metrics through which we can evaluate a regression model in keras?

What are the metrics through which we can evaluate a regression model in keras?

What are the metrics through which we can evaluate a regression model in keras?

This recipe explains what are the metrics through which we can evaluate a regression model in keras


Recipe Objective

The metrics through which we can evaluate a regression model in keras Keras Have provided us many ways to evaluate the Regression and Classification model when we train a deep Learning Model. But our focus is to evaluate a Regression Model

Certain types of Metrics can be used for regression Models: Mean Squared Error Mean Absolute Error Mean Absolute Percentage Error Cosine Proximity We will show you the implementation of all Metrics and visualize the Mean Squared Error and Mean Absolute Error.

Step 1- Importing Libraries

from numpy import array from keras.models import Sequential from keras.layers import Dense from matplotlib import pyplot

Step 2- Creating arrays

We will Define two Arrays Let's consider y_pred is our predicted array and y_actual is our actual array.

# prepare sequence y_pred = array([0.4, 0.6, 0.8, 1.0, 0.5, 0.4, 0.3, 0.2, 0.1, 0.8]) y_actual = array([-0.4, -0.3, 0.8, 0.9, 0, 1.2, 0.3, 2.4, 0.3, 0.3])

Step 3- We will add layers and other parameters to create our model.

We will define all the Metrics while compiling the model as you can see below.

# create model model = Sequential() model.add(Dense(2, input_dim=1)) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['mse', 'mae','mape', 'cosine_proximity'])

Step 4- Training our model

We will train our model with the defined parameters

# train model history = model.fit(y_pred, y_actual, epochs=50, batch_size=len(y_pred), verbose=2)

Step 5- We will plot our models (MSE and MAE)

# plot metrics pyplot.plot(history.history['mse']) pyplot.plot(history.history['mae']) pyplot.show()

Relevant Projects

Predict Credit Default | Give Me Some Credit Kaggle
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.

Loan Eligibility Prediction using Gradient Boosting Classifier
This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

Music Recommendation System Project using Python and R
Machine Learning Project - Work with KKBOX's Music Recommendation System dataset to build the best music recommendation engine.

Solving Multiple Classification use cases Using H2O
In this project, we are going to talk about H2O and functionality in terms of building Machine Learning models.

Predict Macro Economic Trends using Kaggle Financial Dataset
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

Build an Image Classifier for Plant Species Identification
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.

Choosing the right Time Series Forecasting Methods
There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.

Forecast Inventory demand using historical sales data in R
In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.

Zillow’s Home Value Prediction (Zestimate)
Data Science Project in R -Build a machine learning algorithm to predict the future sale prices of homes.

Mercari Price Suggestion Challenge Data Science Project
Data Science Project in Python- Build a machine learning algorithm that automatically suggests the right product prices.