In General:

To [...] an m×n matrix by an n×p matrix, the ns must be the same,
and the result is an m×p matrix.

• tf.keras.losses.MSE()
• tf.metrics.mean_square_error()
• when larger errors are [...] that smaller errors

Tracking your experiments

One really good habit is to track the results of your experiments. There are tools to help us!

Resource: Try:

• [...] - a component of Tensorflow library to help track modelling experiments
• Weights & Biase

X_train, X_test = tf.[...](tf.random.shuffle(X, seed=42), num_or_size_splits=[40, 10])

def plot_predictions(train_data = X_train, train_labels = y_train, test_data = X_test, test_labels = y_test, predictions = y_pred): """ Plots training data, testing_data """ plt.figure(figsize=(10, 7)) plt.scatter(train_data, train_labels, c="blue", label='Training data') plt.scatter(test_data, test_labels, c="green", label="Testing data") plt.scatter(test_data, predictions, c="red", label="Predictions") plt.legend();

Common regression evaluation metrics

Multiclass image classificaton: pizza, steak, sushi

Input_shape = [[...]] - single image

Input shape = [32, 224, 224, 3] - common batch size of images

32 is a common batch size

# Get the patterns of a layer in our network

weights, biases = model_35.layers[1].[...]()

Preprocessing data

ct = make_column_transformer((OneHotEncoder(dtype="int32"), ['Sex']), remainder="passthrough") #other columns unchangaed
ct.fit(X_train)

X_train_transformed = ct.transform(X_train)
X_test_transformed = ct.transform(X_test)

Predictions

valuation_predicts = model.predict(X_valuation_transformed)

(array([[ 9.441547],
        [10.451973],
        [10.48082 ],
        ...,
        [10.401164],
        [13.13452 ],
        [ 8.081818]], dtype=float32),
 (60411, 1))

valuation_predicts_squeezed = tf.[...](valuation_predicts)

submitt_data = pd.DataFrame({'id': data_test['id'],
                             'Rings': valuation_predicts_squeezed})

#Make sure that Min number of rings = 1
submitt_data.loc[submitt_data['Rings'] < 1, 'Rings'] = 1
submitt_data

# Create training and test datasets
#my way:

from sklearn.model_selection import train_test_split 

[...] = train_test_split(X, y, test_size = 0.2, random_state=42)

For imbalanced class problems. Higher precision leads to [...] false positives.