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format that predictions will take. For example, below are some common predictive modeling problem types and the structure and standard activation function that you can use in the output layer: <span>Regression: Linear activation function, or linear , and the number of neurons matching the number of outputs. This is the default activation function used for neurons in the Dense layer. Binary Classification (2 class) : Logistic activation function, or sigmoid , and one neuron the output layer. Multiclass Classification (> 2 class) : Softmax activation function, or

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sits at the core of BTYD models – while ”alive”, customers make purchases until they drop out – gives these models robust predictive power, especially on the aggregate cohort level, and over a <span>long time horizon. <span>

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X_train, X_test = tf.split(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_

iment Evaluation model: visualize What can visualize? the data model itself the training of a model predictions ## The 3 sets (or actually 2 sets: training and test set) tf.random.set_seed(999) <span>X_train, X_test = tf.split(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 keyboard_arrow_down Introduction For regression problems: MAE tf.keras.losses.MAE() tf.metrics.mean_absolute_error() great starter metrics for any regression problem MSE tf.keras.losses

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The choice of activation function depending on problem Regression: Linear activation function and the number of neurons matching the number of outputs. This is the default activation function used for neurons in the Dense layer.

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As the number of hyperparameters and their range grow, the search space becomes exponentially complex, and tuning the models manually or by grid-search becomes impractical . Bayesian optimization for hyperparameter tuning provides hyperparameters (step 1) iteratively based on previous performance

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Association still flows in exactly the same way in Bayesian networks as it does in causal graphs, though. In both, association flows along chains and forks, unless a node is conditioned on. And in both, a collider blocks the flow of association, unless it is conditioned on.

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ignorability - we can make this assumption realistic by running randomized experiments, which force the treatment to not be caused by anything but a coin toss, so then we have the causal structure s

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acronym noun [ C ] UK /ˈækrəʊnɪm/ US a word made from the first letters of other words

acronym noun [ C ] UK /ˈækrəʊnɪm/ US a word made from the first letters of other words akronim, skrótowiec AIDS is the acronym for 'acquired immune deficiency syndrome'.

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Since customer transactions occur sequentially, they can be modeled as a sequence prediction task using an RNN as well, where all firm actions and customer responses are represented by elements in a vector.

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h a data-driven framework to predict whether a consumer is going to purchase an item within a certain time frame using e-commerce retail data. To model this relationship, we create a sequential <span>time-series data for all relevant consumer-item combinations. We then build generalized non-linear models by generating features at the intersection of consumer, item, and time. <span>

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screte hidden states (where N is typically small) and, therefore, has only log 2 (N) bits of information available to capture the sequence history (Brown & Hinton, 2001). On the other hand, <span>the RNN has distributed hidden states, which means that each input generally results in changes across all the hidden units of the RNN (Ming et al., 2017). RNNs combine a large number of distributed hidden states with nonlinear dynamics to update these hidden states, thereby allowing it to have a more substantial representational capacity

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import tensorflow as tf #stop training after reaching accuract of 0.99 class MyCallback(tf.keras.callbacks.Callback): def on_epoch_end(self, epoch, logs={}): if logs.get('accuracy')>=0.99: print('\nAccuracy 0.99 achieved') self.model.stop_training = True

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we must avoid introducing incidental complexity, or complexity that is not necessitated by the problem itself but is an unwanted artifact of a chosen approach. Incidental complexity is a huge problem for real-world data science because we have to deal with such a high level of inherent complexity that distinguishing between real problems and