Author: Anonymous

Consider the following code to identify outliers:1)from sklearn.ensemble import IsolationForest 2)iforest = IsolationForest(random_state=0, contamination=0.05).fit(x) 3)y_iforest = iforest.predict(x) 4)idx = (y_iforest==1) 5)x_no_outliers = x[idx, :] 6)y_no_outliers = y[idx] 7)print(f’Total Number of Observations: {len(y)}’) 8)print(f’Total Number of Outliers: {len(y) – len(y_no_outliers)}’)Which line has the label without outliers?

The word ‘like’ has become hackneyed. Rather than being used sparingly, people use it as verbal filler between pauses of actual verbal information. The word hackneyed as it is used in the sentence most nearly means

Suppose we consider a one-step binomial tree model to price a derivative that pays in the up state and in the down state.  What is the terminal expected payoff of this derivative?

Suppose we are trying to implement an ML model to capture a highly nonlinear relationship between two variables. Which of the following options does not improve the model’s ability to capture nonlinearities?

What is the last element of x?x = np.linspace(0, 0.4, 9)

Which of the following statements is true according to the passage?

Using the following graph, find an Euler circuit that begins and ends with vertex B.

For the following floor plan, is it possible to find a route that begins in one room, passes through each doorway exactly once, and ends in the starting room?

What is the length of the following array? x = np.linspace(-1, 7, 23)

Consider the following code that we used to classify the income of adults in the US.inputs = tf.keras.layers.Dense(units=32, activation=’relu’, input_shape=[len(features.columns)]) hidden = tf.keras.layers.Dense(units=32, activation=’relu’) outputs = tf.keras.layers.Dense(units=2, activation=TO BE FILLED) model = tf.keras.Sequential([inputs, hidden, outputs]) loss = ‘sparse_categorical_crossentropy’ optimizer = tf.keras.optimizers.RMSprop(0.001) model.compile(loss=loss, optimizer=optimizer, metrics=[‘accuracy’])What was the activation function used on the output layer in this deep neural network?