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add params for avoiding warn and improving perf.

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rickiepark
2018-01-30 17:19:37 +09:00
parent 0f46b85009
commit 385d635e92
3 changed files with 8 additions and 8 deletions
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8
04_training_linear_models.ipynb
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@@ -452,7 +452,7 @@
"outputs": [],
"source": [
"from sklearn.linear_model import SGDRegressor\n",
"sgd_reg = SGDRegressor(n_iter=50, penalty=None, eta0=0.1, random_state=42)\n",
"sgd_reg = SGDRegressor(max_iter=50, penalty=None, eta0=0.1, random_state=42)\n",
"sgd_reg.fit(X, y.ravel())"
]
},
@@ -880,7 +880,7 @@
},
"outputs": [],
"source": [
"sgd_reg = SGDRegressor(penalty=\"l2\", random_state=42)\n",
"sgd_reg = SGDRegressor(max_iter=5, penalty=\"l2\", random_state=42)\n",
"sgd_reg.fit(X, y.ravel())\n",
"sgd_reg.predict([[1.5]])"
]
@@ -981,7 +981,7 @@
"X_train_poly_scaled = poly_scaler.fit_transform(X_train)\n",
"X_val_poly_scaled = poly_scaler.transform(X_val)\n",
"\n",
"sgd_reg = SGDRegressor(n_iter=1,\n",
"sgd_reg = SGDRegressor(max_iter=1,\n",
" penalty=None,\n",
" eta0=0.0005,\n",
" warm_start=True,\n",
@@ -1030,7 +1030,7 @@
"outputs": [],
"source": [
"from sklearn.base import clone\n",
"sgd_reg = SGDRegressor(n_iter=1, warm_start=True, penalty=None,\n",
"sgd_reg = SGDRegressor(max_iter=1, warm_start=True, penalty=None,\n",
" learning_rate=\"constant\", eta0=0.0005, random_state=42)\n",
"\n",
"minimum_val_error = float(\"inf\")\n",