Your parents told you to look both ways before you cross the street. It is kind of idea that there’s useful information to the left and the right that you’d like to know about before you do anything.
K-Fold cross-validation has a single parameter called k that refers to the number of groups that a given dataset is to be split(fold). First Split the dataset into k groups than take the group as a test data set the remaining groups as a training data set. In this tutorial, we create a simple classification keras model and train and evaluate using K-fold cross-validation. Download Dataset This guide uses Iris Dataset to categorize flowers by species. This is a popular dataset for a beginner in machine learning classification problems. Download the […]
it’s important to have the same vector space between training & predicting. The most common way is to save tokenizer and load the same tokenizer at predicting time using pickle.
We build a Linear Regression model to predict the Celsius degree from given Fahrenheit degree.TensorFlow Keras is our API for building Linear Regression models and for running Machine Learning models.
In this tutorial, you will discover how to develop a convolutional neural network to classify satellite images of the Amazon forest.
To compute the ROC curve, you first need to have a set of predictions probability, so they can be compared to the actual targets. You could make predictions on the validation set.
Combine precision and recall into a single metric called the F1 score, in particular, if you need a simple way to compare classifiers.
Accuracy is not a reliable metric for the model performance, because it will yield misleading results if the validation data set is unbalanced.
The rule of thumb is to split your data across several large files. If you have too many files, the time to access each file might start getting higher.
In this tutorial, we’ll be demonstrating how to predict an image on trained keras model. So our goal has been to build a CNN