Question: I was new to Machine Learning and stuck with this.

When I was trying to implement polynomial regression in Linear model, like using several degree of polynomials range(1,10) and get different MSE. I actually use `GridsearchCV` method to find the best parameters for polynomial.

```
from sklearn.model_selection import GridSearchCV

poly_grid = GridSearchCV(PolynomialRegression(), param_grid, cv=10, scoring='neg_mean_squared_error')

```

I don't know how to get the the above `PolynomialRegression()` estimator. One solution I searched was:

```
import numpy as np
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import make_pipeline

def PolynomialRegression(degree=2, **kwargs):
    return make_pipeline(PolynomialFeatures(degree), LinearRegression(**kwargs))

param_grid = {'polynomialfeatures__degree': np.arange(10), 'linearregression__fit_intercept': [True, False], 'linearregression__normalize': [True, False]}

poly_grid = GridSearchCV(PolynomialRegression(), param_grid, cv=10, scoring='neg_mean_squared_error')

```

But it didn't even generate any result.

Answer: 
Try this: 

```html
 <div style="display:flex;">
    <img src="https://i.imgur.com/hHzrRsf.jpg" style="width:160px;align-self: center;">
</div>
```

By default, `align-self` is set to `stretch` and remove the original height of your image.
```html
<div style="display:flex;">
<img src="https://i.imgur.com/hHzrRsf.jpg" style="width:160px; height:100%;"></div>
</div>
```

You can use Hight 100% that will be easy for you to understand