Explainable AI in Python
Fouad Trad
Machine Learning Engineer
GRE Score | TOEFL Score | University Rating | SOP | LOR | CGPA | Chance of Admit | Accept |
---|---|---|---|---|---|---|---|
337 | 118 | 4 | 4.5 | 4.5 | 9.65 | 0.92 | 1 |
324 | 107 | 4 | 4 | 4.5 | 8.87 | 0.76 | 1 |
316 | 104 | 3 | 3 | 3.5 | 8 | 0.72 | 1 |
322 | 110 | 3 | 3.5 | 2.5 | 8.67 | 0.8 | 1 |
314 | 103 | 2 | 2 | 3 | 8.21 | 0.45 | 0 |
The data exists in:
X_train
, y_train
from sklearn.neural_network import MLPClassifier model = MLPClassifier(hidden_layer_sizes=(10,10))
model.fit(X_train, y_train)
from sklearn.inspection import permutation_importance
result = permutation_importance(model,
X_train, y_train,
n_repeats=10,
random_state=42,
scoring='accuracy')
print(result.importances_mean)
[0.16213568 0.13831658 0.10575377 0.10522613 0.11741206 0.20072864]
import matplotlib.pyplot as plt
plt.bar(X_train.columns,
result.importances_mean)
import matplotlib.pyplot as plt
plt.bar(X_train.columns,
result.importances_mean)
plt.bar(X_train.columns, np.abs(log_reg.coef_[0]))
Explainable AI in Python