Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

import numpy as np
import torch
import torch.optim as optim
import torch.nn as nn
import torch.functional as F
from torch.utils.data import DataLoader, TensorDataset
from sklearn.datasets import make_moons
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, roc_curve, \
precision_recall_curve, auc
from stepbystep.v0 import StepByStep
A Simple Classification Problem
It is time to handle a different class of problems: classification problems (pun
intended). In a classification problem, we’re trying to predict which class a data
point belongs to.
Let’s say we have two classes of points: They are either red or blue. These are the
labels (y) of the points. Sure enough, we need to assign numeric values to them. We
could assign zero to red and one to blue. The class associated with zero is the
negative class, while one corresponds to the positive class.
In a nutshell, for binary classification, we have:
Color Value Class
Red 0 Negative
Blue 1 Positive
IMPORTANT: In a classification model, the output is the
predicted probability of the positive class. In our case, the model
will predict the probability of a point being blue.
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