Classification
In this section, we will demonstrate an end-to-end pipeline that can be used as a template for handling image classification problems. We will walk through the necessary steps, from data preparation, to model setup and estimation, to results visualization. Apart from being informative (and cool), this last step can also be very useful if you need to examine your code in-depth to get a better understanding of the performance.
We will continue using the data from the Cassava Leaf Disease Classification contest (https://www.kaggle.com/c/cassava-leaf-disease-classification).
As usual, we begin by loading the necessary libraries:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import datetime
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import tensorflow as tf
from tensorflow.keras import models, layers
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing...
