updated epoch and training size
@@ -40,7 +40,7 @@ t0 = timeit.default_timer()
solver = "saga"
# Turn down for faster run time
-n_samples = 10000
+n_samples = 5000
X, y = fetch_20newsgroups_vectorized(subset="all", return_X_y=True)
X = X[:n_samples]
@@ -58,8 +58,8 @@ print(
)
models = {
- "ovr": {"name": "One versus Rest", "iters": [1, 2, 4]},
- "multinomial": {"name": "Multinomial", "iters": [1, 3, 7]},
+ "ovr": {"name": "One versus Rest", "iters": [1, 2, 3]},
+ "multinomial": {"name": "Multinomial", "iters": [1, 2, 5]},
}
for model in models:
