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In machine learning, classification tasks involve predicting categories or labels. For instance, classifying emails as 'spam' or 'not spam'.
df['Letter_Grade'] = df['Letter_Grade'].replace({'A': 4, 'B': 3, 'C': 2, 'D': 1, 'F': 0})
Label encoding converts categories into numeric values. For example, assigning '1' for 'disease' and '0' for 'no disease'.
df = pd.get_dummies(df, columns=['Accommodation'], dtype=int)
One-hot encoding converts categorical data into binary columns. Each category gets its own column with a '1' for presence and '0' for absence.
One-hot encoding example: For 'Rental', 'Dorm', 'With Family', 'Other', each category is turned into a separate column with '1' or '0'.
The sigmoid function converts a value to a probability between 0 and 1, helping in binary classification tasks.
model = nn.Sequential(nn.Linear(5, 3), nn.ReLU(), nn.Linear(3, 1), nn.Sigmoid())
In binary classification, a threshold helps determine the class based on the probability output. For instance, if the threshold is 0.5, values above it are classified as '1' and below as '0'.
threshold = 0.5 # Adjust the threshold as needed
classification = int(probability >= threshold)
This loss function measures how well the model's predicted probabilities match the actual labels in binary classification.
def BCELoss(p, y):
if y == 1: # True label is 1
return -np.log(p)
else: # True label is 0
return -np.log(1 - p)
from torch import nn
loss = nn.BCELoss()
Stochastic Gradient Descent (SGD) is an optimization technique used to minimize the loss function and update model weights in PyTorch.
import torch.optim as optim
optimizer = optim.SGD(model.parameters(), lr=0.001)
Accuracy is a metric that measures how many predictions were correct out of the total number of predictions.
Accuracy = number_of_correct_predictions / total_number_of_predictions
These metrics help evaluate model performance by measuring true positives, false positives, and false negatives.
from sklearn.metrics import precision_score, recall_score, f1_score
precision = precision_score(y_true, y_pred)
recall = recall_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred)
Softmax converts model outputs into probabilities for each class in multiclass classification tasks. Each class gets a probability score that sums up to 1.
import torch
output = torch.nn.Softmax(dim=1)(model_output)
The argmax function selects the class with the highest probability from the softmax output, determining the final classification.
predicted_class = torch.argmax(model_output, dim=1)
Cross-entropy loss measures how well the predicted probabilities match the actual classes in multiclass classification.
from torch import nn
criterion = nn.CrossEntropyLoss()
loss = criterion(predictions, targets)
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