How variable alpha changes SGDRegressor behavior for outlier?

I am using SGDRegressor with a constant learning rate and default loss function. I am curious to know how changing the alpha parameter in the function from 0.0001 to 100 will change regressor behavior. Below is the sample code I have:

from sklearn.linear_model import SGDRegressor

out=[(0,2),(21, 13), (-23, -15), (22,14), (23, 14)] 
alpha=[0.0001, 1, 100]
N= len(out)
plt.figure(figsize=(20,15))
j=1

for i in alpha:
    X= b * np.sin(phi)   #Since for every alpha we want to start with original dataset, I included X and Y in this section
    Y= a * np.cos(phi)
    for num in range(N):
        plt.subplot(3, N, j)
        X=np.append(X,out[num][0]) # Appending outlier to main X
        Y=np.append(Y,out[num][1]) # Appending outlier to main Y
        j=j+1  # Increasing J so we move on to next plot
        model=SGDRegressor(alpha=i, eta0=0.001, learning_rate='constant',random_state=0)
        model.fit(X.reshape(-1, 1), Y) # Fitting the model

        plt.scatter(X,Y)
        plt.title(alpha = + str(i) +  |  + Slope : + str(round(model.coef_[0], 4))) #Adding title to each plot
   
        abline(model.coef_[0],model.intercept_)  # Plotting the line using abline function
    
plt.show()

As shown above I had the main datset of X and Y and in each iteration, I am adding a point as an outlier to the main dataset and train the model and plot regression line (hyperplane). Below you can see the result for different values of alpha:

I am looking at results and am still confused and can't make solid conclusion as how alhpa parameter changes the model? what's the effect of alpha? is it causing overfitting? underfitting?