The Precision-Recall Trade-Off

import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import precision_score, recall_score
thresholds = np.linspace(0, 1, 100)
precision_scores = []
recall_scores = []
for threshold in thresholds: adjusted_predictions = [1 if p > threshold else 0 for p in predictions] precision_scores.append(precision_score(y_val, adjusted_predictions)) recall_scores.append(recall_score(y_val, adjusted_predictions))plt.plot(thresholds, precision_scores, label="precision")
plt.plot(thresholds, recall_scores, label="recall")
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Datascience George

Datascience George

Data scientist learning at Flat Iron School

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