Question 1

You are evaluating a logistic regression model using the ROC curve. Which of the following is true regarding the Area Under the Curve (AUC)?

Options :

A : A model with an AUC of 0 performs better than one with an AUC of 1

B : A higher AUC indicates that the model has higher bias

C : A model with an AUC closer to 1 performs better than one closer to 0

D : A model with an AUC of 0.5 is an excellent model

Answer: C

Question 2

While evaluating a logistic regression model using a confusion matrix, you notice that the True Positive rate is significantly higher than the True Negative rate. What does this observation imply?

Options :

A : The model is better at identifying the positive class than the negative class

B : The model performs equally well for both classes

C : The model has a high level of bias

D : The model is overfitting

Answer: A

Question 3

Your dataset consists of geographical data points for different species of birds. You want to visualize the distribution of these species on a geographical map. Which Python library would be most appropriate for this kind of visualization?

Options :

A : Plotly

B : Geopandas

C : Seaborn

D : Matplotlib

Answer: B

Question 4

You have a Pandas DataFrame that contains missing values. Which of the following methods will replace all NaNs with zeros?

Options :

A : df.fillna(value=0)

B : df.replace(to_replace=np.nan, value=0)

C : df['column_name'] = 0

D : df.dropna(subset=['column_name'])

Answer: A

Question 5

You have a DataFrame named df that has three columns: 'A', 'B', 'C'. The DataFrame has 100 rows. Your task is to create a DataFrame where each column 'A', 'B', 'C' is transformed into 3 columns each: 'A_min', 'A_max', 'A_mean', 'B_min', 'B_max', 'B_mean', 'C_min', 'C_max', 'C_mean'. Which of the following code snippets accomplishes this task?

Options :

A : new_df = df.groupby(['A', 'B', 'C']).agg(['min', 'max', 'mean'])

B : new_df = df.apply(['min', 'max', 'mean'])

C : new_df = df.pivot(columns=['A', 'B', 'C'], values=['min', 'max', 'mean'])

D : new_df = df.aggregate(

{

'A': ['min', 'max', 'mean'],

'B': ['min', 'max', 'mean'],

'C': ['min', 'max', 'mean'],

}

)

Answer: D

Confidently Practice Online with Free PCED Exam Cram

Buying Options: