What’s the idea of Decision Tree Regression?
The basic intuition behind a decision tree is to map out all possible decision paths in the form of a tree. It can be used for classification (note) and regression. In this post, let’s try to understand the regression.
DT Regression is similar to DT Classification, however we use Mean Square Error (MSE, default) or Mean Absolute Error (MAE) instead of crossentropy or Gini impurity to determine splits.
$\begin{aligned} \text{MSE} &= \frac{1}{n} \sum_{i=1}^{n} (y_i  \bar{y}_i)^2, \\ \text{MAE} &= \frac{1}{n}\sum_{i=1}^n \vert y_i  \bar{y}_i \vert. \end{aligned}$
Suppose that we have a dataset $S$ like in the figure below,
An example of dataset $S$.
A decision tree we want.
Some basic concepts
 Splitting: It is a process of dividing a node into two or more subnodes.
 Pruning: When we remove subnodes of a decision node, this process is called pruning.
 Parent node and Child Node: A node, which is divided into subnodes is called parent node of subnodes where as subnodes are the child of parent node.
Other aspects of decision tree algorithm, check this note.
Looking for an example like in the post of decision tree classifier? Check this! Below are a short algorithm,

Calculate the Standard Deviation ($SD$) of the current node (let’s say $S$, parent node) by using MSE or MAE,
$\begin{aligned} SD(S) &= \frac{1}{n} \sum_{i=1}^{n} (y_i  \bar{y}_i)^2, \\ \text{or } SD(S) &= \frac{1}{n}\sum_{i=1}^n \vert y_i  \bar{y}_i \vert, \end{aligned}$
where $y_i\in$ the target values (Hours Played in the above example), $\bar{y}=\frac{\Sigma y}{n}$ is the mean value and $n$ is the number of examples in this node.

Check the stopping conditions (we don’t need to make any split at this node) to stop the split and this node becomes a leaf node. Otherwise, go to step 3.
 The minimum number of samples required to split an internal node, use
min_samples_split
in scikitlearn.  The maximum depth of the tree, use
max_depth
in scikitlearn.  A node will be split if this split induces a decrease of the impurity greater than or equal to this value, use
min_impurity_decrease
in scikitlearn.  Its coefficient of variation ($\frac{SD(S)}{\bar{y}}$) is less than a certain threshold.
 The minimum number of samples required to split an internal node, use

Calculate the Standard Deviation Reduction (SDR) after splitting node $S$ on each attribute (for example, consider attribute $O$). The attribute w.r.t. the biggest SDR will be chosen!
$\underbrace{SDR(S,O)}_{\text{Standard Deviation Reduction}} = \underbrace{SD(S)}_{\text{SD before split}}  \underbrace{\sum_j P(O_j  S) \times SD(S,O_j)}_{\text{weighted SD after split}}$
where $j \in$ number of different properties in $O$ and $P(O_j)$ is the propability of property $O_j$ in $O$. Note that, $SD(S,O_j)$ means the SD of node $O_j$ which is also a child of node $S$.

After splitting, we have new child nodes. Each of them becomes a new parent node in the next step. Go back to step 1.
Using Decision Tree Regression with Scikitlearn
Load and create
Load the library,
from sklearn.tree import DecisionTreeRegressor
Create a decision tree (other parameters):
# mean squared error (default)
reg = DecisionTreeRegressor() # criterion='mse'
# mean absolute error
reg = DecisionTreeRegressor(criterion='mae')
An example,
from sklearn import tree
X = [[0, 0], [2, 2]]
y = [0.5, 2.5]
reg = tree.DecisionTreeRegressor()
reg = reg.fit(X, y) # train
array([0.5])
Plot and save plots
Plot the tree (You may need to install Graphviz first. Don’t forget to add its installed folder to $path
),
from IPython.display import Image
import pydotplus
dot_data = tree.export_graphviz(reg, out_file=None,
rounded=True,
filled=True)
graph = pydotplus.graph_from_dot_data(dot_data)
Image(graph.create_png())
Save the tree (follows the codes in “plot the tree”)
graph.write_pdf("tree.pdf") # to pdf
graph.write_png("thi.png") # to png
References
 Skikitlearn. Decision Tree Regressor official doc.
 Saed Sayad. Decision Tree  Regression.