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Update HW_4.ipynb #3

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4 changes: 2 additions & 2 deletions lesson_4_Training/HW_4.ipynb
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"id": "QhSnTaNuzOd4"
},
"source": [
"Create create Y, X, and Z samples from problem 1 with 100,000,000 observations (100 million). Measure how long does it take to estimate linear regression of Y on X and Z using: Stochastic Gradient Descent, Batch Gradient Descent, Linear Regression from sklearn, and Linear regression estimated using matrix multiplication $$\\hat{\\theta} = (\\pmb{X}^T \\cdot \\pmb{X})^{-1} \\cdot \\pmb{X}^T \\cdot \\pmb{y}$$, and Linear regression estimated preudo-inverse. For Batch Gradient Descent use 1000 iterations and eta = 0.01. For Stochastic Gradient Descent use 5 iterations and eta = 0.01. Use SGDRegressior imported from sklearn.model.\n",
"Create Y, X, and Z samples from problem 1 with 100,000,000 observations (100 million). Measure how long does it take to estimate linear regression of Y on X and Z using: Stochastic Gradient Descent, Batch Gradient Descent, Linear Regression from sklearn, and Linear regression estimated using matrix multiplication $$\\hat{\\theta} = (\\pmb{X}^T \\cdot \\pmb{X})^{-1} \\cdot \\pmb{X}^T \\cdot \\pmb{y}$$, and Linear regression estimated preudo-inverse. For Batch Gradient Descent use 1000 iterations and eta = 0.01. For Stochastic Gradient Descent use 5 iterations and eta = 0.01. Use SGDRegressior imported from sklearn.model.\n",
"\n",
"For each estimation import **MSE** and **time**"
]
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},
"source": [
"# Problem 6\n",
"Recreate a X,Y data from problem 1 using 500 observations. Create a 25-degree polynomial for X and scale the data using standard scaler. Estimate the regression model predicting Y via Ridge regression. Calculate MSE (average 'neg_mean_squared_error') using cross-validation with cv = 3. Find optimal alpha by looping from 0.0001 to 1 using step size 0.0001."
"Recreate the X,Y and Z data from problem 1 using 500 observations. Create a 25-degree polynomial for X and scale the data using standard scaler. Estimate the regression model predicting Y via Ridge regression. Calculate MSE (average 'neg_mean_squared_error') using cross-validation with cv = 3. Find optimal alpha by looping from 0.0001 to 1 using step size 0.0001."
]
},
{
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