Author: Yury Kashnitsky. All content is distributed under the Creative Commons CC BY-NC-SA 4.0 license.

**Same assignment as a Kaggle Kernel + solution.**

**Fill in the missing code and choose answers in this web form.**

In [1]:

```
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
from sklearn.metrics.regression import mean_squared_error
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.linear_model import LinearRegression, LassoCV, Lasso
from sklearn.ensemble import RandomForestRegressor
```

**We are working with UCI Wine quality dataset (no need to download it – it's already there, in course repo and in Kaggle Dataset).**

In [2]:

```
data = pd.read_csv('../../data/winequality-white.csv', sep=';')
```

In [3]:

```
data.head()
```

Out[3]:

In [4]:

```
data.info()
```

**Separate the target feature, split data in 7:3 proportion (30% form a holdout set, use random_state=17), and preprocess data with StandardScaler.**

In [5]:

```
# y = None # you code here
# X_train, X_holdout, y_train, y_holdout = train_test_split # you code here
# scaler = StandardScaler()
# X_train_scaled = scaler.fit_transform # you code here
# X_holdout_scaled = scaler.transform # you code here
```

**Train a simple linear regression model (Ordinary Least Squares).**

In [6]:

```
# linreg = # you code here
# linreg.fit # you code here
```

**Question 1: What are mean squared errors of model predictions on train and holdout sets?**

In [7]:

```
# print("Mean squared error (train): %.3f" % # you code here
# print("Mean squared error (test): %.3f" % # you code here
```

**Sort features by their influence on the target feature (wine quality). Beware that both large positive and large negative coefficients mean large influence on target. It's handy to use pandas.DataFrame here.**

**Question 2: Which feature this linear regression model treats as the most influential on wine quality?**

In [8]:

```
# linreg_coef = pd.DataFrame # you code here
# linreg_coef.sort_values # you code here
```

**Train a LASSO model with $\alpha = 0.01$ (weak regularization) and scaled data. Again, set random_state=17.**

In [9]:

```
# lasso1 = Lasso # you code here
# lasso1.fit # you code here
```

**Which feature is the least informative in predicting wine quality, according to this LASSO model?**

In [10]:

```
# lasso1_coef = pd.DataFrame # you code here
# lasso1_coef.sort_values # you code here
```

**Train LassoCV with random_state=17 to choose the best value of $\alpha$ in 5-fold cross-validation.**

In [11]:

```
# alphas = np.logspace(-6, 2, 200)
# lasso_cv = LassoCV # you code here
# lasso_cv.fit # you code here
```

In [12]:

```
# lasso_cv.alpha_
```

**Question 3: Which feature is the least informative in predicting wine quality, according to the tuned LASSO model?**

In [13]:

```
# lasso_cv_coef = pd.DataFrame # you code here
# lasso_cv_coef.sort_values # you code here
```

**Question 4: What are mean squared errors of tuned LASSO predictions on train and holdout sets?**

In [14]:

```
# print("Mean squared error (train): %.3f" % # you code here
# print("Mean squared error (test): %.3f" % # you code here
```

**Train a Random Forest with out-of-the-box parameters, setting only random_state to be 17.**

In [15]:

```
# forest = RandomForestRegressor # you code here
# forest.fit # you code here
```

**Question 5: What are mean squared errors of RF model on the training set, in cross-validation (cross_val_score with scoring='neg_mean_squared_error' and other arguments left with default values) and on holdout set?**

In [16]:

```
# print("Mean squared error (train): %.3f" % # you code here
# print("Mean squared error (cv): %.3f" % # you code here
# print("Mean squared error (test): %.3f" % # you code here
```

**Tune the max_features and max_depth hyperparameters with GridSearchCV and again check mean cross-validation MSE and MSE on holdout set.**

In [17]:

```
# forest_params = {'max_depth': list(range(10, 25)),
# 'min_samples_leaf': list(range(1, 8)),
# 'max_features': list(range(6,12))}
# locally_best_forest = GridSearchCV # you code here
# locally_best_forest.fit # you code here
```

In [18]:

```
# locally_best_forest.best_params_, locally_best_forest.best_score_
```

**Question 6: What are mean squared errors of tuned RF model in cross-validation (cross_val_score with scoring='neg_mean_squared_error' and other arguments left with default values) and on holdout set?**

In [19]:

```
# print("Mean squared error (cv): %.3f" % # you code here
# print("Mean squared error (test): %.3f" % # you code here
```

**Output RF's feature importance. Again, it's nice to present it as a DataFrame.**

**Question 7: What is the most important feature, according to the Random Forest model?**

In [20]:

```
rf_importance = pd.DataFrame # you code here
rf_importance.sort_values # you code here
```

Out[20]:

**Make conclusions about the perdormance of the explored 3 models in this particular prediction task.**