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The process for modifying the classification thresholds in a multiclass classification situation involves the following steps:

1. Understand the classification metrics: Before modifying the classification thresholds, it is essential to understand the classification metrics used to evaluate the model's performance. The most common metrics used in multiclass classification are accuracy, precision, recall, and F1-score.

2. Analyze the model's confusion matrix: The confusion matrix displays a summary of the model's classification results. By analyzing the confusion matrix, you can identify which classes the model has difficulty classifying correctly.

3. Identify the threshold values: The threshold is the level of certainty required before classifying the input into a particular class. The default threshold value is usually 0.5, but it can be modified to improve the model's performance. You can identify the threshold values by analyzing the model's predicted probabilities.

4. Adjust the threshold values: To adjust the threshold values, you can either increase or decrease the threshold values to increase the model's sensitivity or specificity, respectively. Increasing the threshold values increases the model's specificity, while decreasing it increases the model's sensitivity.

5. Evaluate the model: After adjusting the threshold values, you must evaluate the model's performance using the classification metrics. If the model's performance improves, the new threshold values can be used; otherwise, you may need to iterate over the process to identify new threshold values.