Information gain calculation problem

Course Q&A Machine Learning Specialization Advanced Learning Algorithms
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Screenshot_1
Screenshot_11260Ă—596 34.5 KB

I can not understand what I have to do.

2

This error means that your implementation of calculating the information gain is not right, what you have to do is go back and redo the function or exercise…

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First of all if I did any mistake then how expected example satisfy with my result. However here is my code that I have written. Please take a look and help me to figure out the solution.

# UNQ_C3
# GRADED FUNCTION: compute_information_gain

def compute_information_gain(X, y, node_indices, feature):
    
    """
    Compute the information of splitting the node on a given feature
    
    Args:
        X (ndarray):            Data matrix of shape(n_samples, n_features)
        y (array like):         list or ndarray with n_samples containing the target variable
        node_indices (ndarray): List containing the active indices. I.e, the samples being considered in this step.
   
    Returns:
        cost (float):        Cost computed
    
    """    
    # Split dataset
    left_indices, right_indices = split_dataset(X, node_indices, feature)
    
#     print("left_indices:", left_indices, "right_indices:" , right_indices, "X ->", X, "feature->", feature)
    
    # Some useful variables
    X_node, y_node = X[node_indices], y[node_indices]
    X_left, y_left = X[left_indices], y[left_indices]
    X_right, y_right = X[right_indices], y[right_indices]
    
    # You need to return the following variables correctly
    information_gain = 0
    
    ### START CODE HERE ###

   # Moderator edit: code removed
    
    ### END CODE HERE ###  
    
    return information_gain
4

Please don’t post your code on the forum. The Code of Conduct does not allow it.
If a mentor needs to see your code, we’ll contact you with instructions.

The other possibility is maybe your compute_entropy() or split_dataset() code doesn’t work correctly.

Passing the test cases built into the notebook does not prove your code is perfect.
The data set used by compute_information_gain_test() is different. So that may expose defects in your other functions.

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5

i have the same issue if it have been resolved for you how can i resolve it

6

Hi @Hamza_AHLAOU

I had faced similar problem. The issue is that compute_entropy function needs to cover all scenarios such as if the dataset is completely pure etc.

The compute_information_gain function uses compute_entropy, and if compute_entropy doesn’t have all edge cases covered, then compute_information_gain will also fail.

Regards
Nagesh

Can’t paste my code here :).

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