Learning tree wilsonville: Learning Tree – Wilsonville | Wilsonville OR Child Care Center

Опубликовано: December 30, 2022 в 2:02 am

Автор: alexxlab

Категории: Miscellaneous

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We offer a friendly, educational and nurturing environment for infants, toddlers, preschool, pre-K, kindergarten and before and after school care in the Tigard, Tualatin, and Canby areas. Learning Tree Preschool encourages early learning and the development of social skills through play, creative activities and other fun exercises. As professional educators, our teachers emphasize the growth of the child as a whole.

We have been in business as independent owners since 1994 when we opened our first Pre School. Since then we have added two other locations. Being owner operated we are better able to connect with the children and families to provide excellent customer service.

What We Do

Infants Through School-Age

We care for children ages 6 weeks to 12 years. Preschool programs offered for ages three to five, after school and summer programs offered for Kindergarten and up.

Classroom Descriptions

What We Offer

We offer a friendly, educational and nurturing environment in the Tigard, Tualatin, and Canby areas. Learning Tree Preschool encourages early learning and the development of social skills through play, creative activities and other fun exercises. As professional educators, our teachers emphasize the growth of the child as a whole.

Additional Services

Trained Staff

Caring and supportive staff have been trained in age appropriate care, safety and education for all ages. Our teachers come from diverse professional and personal backgrounds, but they all share one thing in common: a devotion to your child’s growth and development. To learn more about our staff qualifications click below!

Staff Qualifications

Where To Find Us

Tualatin Learning Tree

18115 SW Boones Ferry Rd Durham OR 97224

Canby Learning Tree

1105 S Elm St

Canby OR 97013

Bonita Road Learning Tree

14440 SW Milton Court

Tigard OR 97224

Hours of Operation and Contact Information

All of our locations are open 6:30 A.M. to 6:00 P.M., Monday through Friday, year round, only closed on major Holidays. Please feel free to contact us at any of our locations:

Tigard 503-684-3772

Tualatin 503-620-9815

Canby 503-263-2345

Fax Number 971-238-0059

Email Us Here

Sample Menu

Immunizations

For more information please use the following links

Covid Protocols

Registration

Learning Tree – Wilsonville | Wilsonville OR Child Care Center

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About the Provider

Little Sunshine’s Playhouse And Preschool – Colorado Springs CO Child Care Center

Description: In business since 1994 we are independently owned private preschools serving the south metro area of Tigard, Tualatin, Wilsonville and our newest location in Canby, Oregon. Our philosophy is to offer a quality early childhood program that is affordable for everyone. We believe it is possible to provide a loving nurturing and educational environment and make it available to as many families as possible. Westrive to create this vision with quality motivated employees and efficient operation that allows us to focus the majority of our resources into the classrooms and on the children in our care.

Program and Licensing Details

Age Range:
3 months to 11 years 11 months
Enrolled in Subsidized Child Care Program:
No
Type of Care:
Full-Time, Part-Time
District Office:
Oregon Employment Department – Child Care Division
District Office Phone:
503-947-1400 (Note: This is not the facility phone number.)

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Local Core Business Supports Neighboring Restaurants Phil Hawkins, Friday, March 27, 2020 The Northwest Hazelnut Company stocks meals for restaurant employees in Aurora, Woodburn and Wilsonville. In accordance with Governor Keith Brown’s directive, Oregonians remain…

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1.10. Decision Trees – scikit-learn

Decision Trees (DT) is a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of the target variable by learning simple decision rules derived from the characteristics of the data. The tree can be considered as a piecewise constant approximation.

For example, in the example below, decision trees are trained from the data to fit a sine curve with a set of if-then-else decision rules. The deeper the tree, the more complex the decision rules and the better the model.

Some advantages of decision trees:

Easy to understand and interpret. Trees can be visualized.
Minor data preparation required. Other methods often require normalizing the data, creating dummy variables, and removing nulls. Note, however, that this module does not support missing values.
The cost of using a tree (i.e., predicting data) is logarithmic in the number of data points used to train the tree.
Can handle both numeric and categorical data. However, the scikit-learn implementation does not yet support categorical variables. Other methods usually specialize in the analysis of datasets containing only one type of variable. See Algorithms for more information.
Capable of handling multiple output problems.
Uses a white box model. If this situation is observable in the model, the explanation of the condition is easily explained using Boolean logic. In contrast, in a black box model (such as an artificial neural network), the results may be more difficult to interpret.
The model can be verified using statistical tests. This makes it possible to take into account the reliability of the model.
Works well even if its assumptions are somewhat violated by the true model from which the data was generated.

Disadvantages of decision trees include:

Decision tree learners can create overly complex trees that do not generalize well. This is called retraining. To avoid this problem, mechanisms such as pruning, setting a minimum number of samples required for a leaf node, or setting a maximum tree depth are needed.
Decision trees can be unstable because small changes to the data can result in a completely different tree. This problem is mitigated by the use of ensemble decision trees.
As you can see from the figure above, decision tree predictions are neither smooth nor continuous, but piecewise constant approximations. Therefore, they are not suitable for extrapolation.
The optimal decision tree learning problem is known to be NP-complete in terms of several aspects of optimality and even for simple concepts. Therefore, practical decision tree learning algorithms are based on heuristic algorithms such as the greedy algorithm in which locally optimal decisions are made at each node. Such algorithms cannot guarantee the return of a global optimal decision tree. This can be mitigated by training multiple trees in a learning ensemble where features and samples are randomly selected with replacement.
There are concepts that are difficult to learn because decision trees don’t express them easily, such as XOR, parity, or multiplexer problems.
Decision tree learners create biased trees if some classes are dominant. Therefore, it is recommended to balance the dataset before fitting to the decision tree.

1.10.1. Classification

DecisionTreeClassifier is a class capable of performing multi-class classification of a dataset.

As with other classifiers, DecisionTreeClassifier takes as input two arrays: an array X , sparse or dense, of shape (n_samples, n_features) containing training samples, and an array Y of integer values, of shape (n_samples,) containing class labels for training samples:

 >>> from sklearn import tree
>>> X = [[0, 0], [1, 1]]
>>> Y = [0, 1]
>>> clf = tree. DecisionTreeClassifier()
>>> clf = clf.fit(X, Y)

Once fitted, the model can be used to predict a class of samples:

 >>> clf.predict([[2., 2.]])
array([1])

In case there are several classes with the same and highest probability, the classifier will predict the class with the lowest index among these classes.

As an alternative to deriving a specific class, one can predict the probability of each class, which is the fraction of class training samples in the leaf:

 >>> clf. predict_proba([[2., 2.]])
array([[0., 1.]])

DecisionTreeClassifier supports both binary (where labels are [-1, 1]) and multiclass (where labels are [0,…, K-1]) classification .

Using the Iris dataset, we can build a tree like this:

 >>> from sklearn.datasets import load_iris
>>> from sklearn import tree
>>> iris = load_iris()
>>> X, y = iris.data, iris.target
>>> clf = tree.DecisionTreeClassifier()
>>> clf = clf.fit(X, y)

After learning, you can plot the tree using plot_tree functions:

 >>> tree. plot_tree(clf)

We can also export the tree to Graphviz format using export_graphviz exporter. If you are using Conda package manager then Graphviz binaries and python package can be installed conda install python-graphviz .

Alternatively, the binaries for graphviz can be downloaded from the home page of the graphviz project, and the Python wrapper can be installed from pypi with pip install graphviz .

Below is an example graphviz export of the above tree trained on the entire iris dataset; the results are saved in the output file iris.pdf :

 >>> import graphviz
>>> dot_data = tree. export_graphviz(clf, out_file=None)
>>> graph = graphviz.Source(dot_data)
>>> graph.render("iris")

The export_graphviz exporter also supports many aesthetic options, including coloring nodes by their class (or regression value) and using explicit variable and class names if needed. Jupyter notebooks also automatically display these plots inline:0003

 >>> dot_data = tree.export_graphviz(clf, out_file=None,
... feature_names=iris.feature_names,
...class_names=iris.target_names,
...filled=True, rounded=True,
...special_characters=True)
>>> graph = graphviz.Source(dot_data)
>>> graph

Alternatively, the tree can also be exported to text format using the function export_text . This method does not require installation of external libraries and is more compact:

 >>> from sklearn.datasets import load_iris
>>> from sklearn.tree import DecisionTreeClassifier
>>> from sklearn.tree import export_text
>>> iris = load_iris()
>>> decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2)
>>> decision_tree = decision_tree.fit(iris.data, iris.target)
>>> r = export_text(decision_tree, feature_names=iris['feature_names'])
>>>print(r)
|--- petal width (cm) <= 0.80
| |---class: 0
|--- petal width (cm) > 0.80
| |--- petal width (cm) <= 1.75
| | |--- class: 1
| |--- petal width (cm) > 1.75
| | |--- class: 2 91.10.2. Regression   Decision trees can also be applied to regression problems using the class  DecisionTreeRegressor  .  
  As in the classification setup, the fit method will take arrays of X and y as arguments, only in this case y is expected to have floating point values instead of integer values: 
  >>> from sklearn import tree
>>> X = [[0, 0], [2, 2]]
>>> y = [0.5, 2.5]
>>> clf = tree.DecisionTreeRegressor()
>>> clf = clf.fit(X, y)
>>> clf.predict([[1, 1]])
array([0.5]) 
 Example: 

 Decision tree regression 

 1.10.3. Multiple output problems 
 The multiple output problem is a supervised learning problem with multiple outputs for prediction i.e. when Y is the 2nd array of shape  (n_samples, n_outputs)  .  
 When there is no correlation between the outputs, a very simple way to solve this problem is to build n independent models, that is, one for each output, and then use these models to independently predict each of the n outputs. However, since it is likely that outputs related to the same input are themselves correlated, it is often best to build a single model capable of predicting all n outputs simultaneously. First, it requires less training time because only one estimator is built. Second, it is often possible to improve the accuracy of summarizing the final grade. 
 As far as decision trees are concerned, this strategy can be easily used to support problems with multiple outputs. This requires the following changes: 

 Store n output values in leaves instead of 1; 
 Use split criteria that calculates the average reduction for all n outputs. 

 This module offers support for multi-output tasks, implementing this strategy in both  DecisionTreeClassifier  and  DecisionTreeRegressor  .  If the decision tree corresponds to an output array Y of shape (n_samples, n_outputs), then the resulting score will be: 

 Print the values of n_output at  predict  ; 
 List the arrays n_output of class probabilities for  predict_proba  . 

 The use of multiple output trees for regression is demonstrated in the Multiple Output Decision Tree Regression section. In this example, the X input is a single real value, and the Y outputs are the sine and cosine of X. 
 The use of multi-output trees for classification is demonstrated in the Completing a face with multiple-output estimates. In this example, the X inputs are the pixels for the top half of the faces, and the Y outputs are the pixels for the bottom half of those faces. 
 Examples: 

 Multi-output decision tree regression 
 Face completion with multi-output estimators 

 Recommendations: 

 M. Dumont et al. conference on theory and applications of computer vision, 2009

 1.
 {2}\log(n_{samples}))$ 
 1.10.5. Practical Tips 

 Decision trees tend to overfit data with a lot of features. Getting the right sample-to-feature ratio is important because a tree with few samples in a high-dimensional space is likely to overfit. 
 Consider dimensionality reduction (PCA, ICA, or Feature selection) first to give your tree a better chance of finding distinguishing features. 
 Understanding the structure of a decision tree will help you better understand how a decision tree makes predictions, which is important for understanding important data features. 
 Visualize your tree during training using the  export  function. Use  max_depth=3  as the initial depth of the tree to see if the tree fits your data and then increase the depth. 
 Remember that the number of samples required to fill a tree doubles for each additional level the tree grows to. Use  max_depth  to control the size of the tree to avoid overfitting.  
 Use  min_samples_split  or  min_samples_leaf  to ensure that multiple samples inform each decision in the tree, controlling which splits are taken into account. A very small number usually means the tree is being retooled, while a large number prevents the tree from learning the data. Try  min_samples_leaf=5  as a starting value. If the sample size is very different, you can use a floating point percentage in the two parameters. While  min_samples_split  can create arbitrarily small leaves,  min_samples_leaf  ensures that each leaf has a minimum size, avoiding finely dispersed, overfitting leaf nodes in regression problems. For a multi-class classification  min_samples_leaf=1  this is often the best choice.
 Note that  min_samples_split  samples are considered directly and independently of  sample_weight  if they are provided (for example, a node with m weighted samples is still treated as having exactly m samples).  Consider  min_weight_fraction_leaf  or  min_impurity_decrease  if weight samples are required for splits. 

 Before training, balance the dataset so that the tree does not drift towards dominant classes. Class balancing can be done by sampling an equal number of samples from each class, or preferably by normalizing the sum of the sample weights (  sample_weight  ) for each class to the same value. Also note that weight-based precut criteria such as  min_weight_fraction_leaf  will be less biased towards dominant classes than criteria that do not know sample weights, such as  min_samples_leaf  . 
 If the samples are weighted, it will be easier to optimize the tree structure using a weight-based pre-cutting criterion, such as  min_weight_fraction_leaf  , which ensures that the leaf nodes contain at least a fraction of the total sum of the sample weights. 
 All decision trees  np. float32  use arrays internally. If the training data is not in this format, a copy of the dataset will be made. 
 If the input matrix X is very sparse, it is recommended to convert it to sparse  csc_matrix  before calling match and sparse  csr_matrix  before calling prediction. The training time can be orders of magnitude smaller for an input sparse matrix compared to a dense matrix when the features have zero values in most samples. 

 1.10.6. Tree algorithms: ID3, C4.5, C5.0 and CART 
 What are the different decision tree algorithms and how do they differ from each other? Which one is implemented in scikit-learn? 
 ID3 (Iterative Dichotomiser 3) was developed by Ross Quinlan in 1986. The algorithm creates a multiway tree by finding, for each node (i.e., greedily), the categorical feature that will yield the largest information gain for categorical purposes. Trees are grown to their maximum size and then a pruning step is usually applied to improve the tree's ability to generalize unseen data.  
 C4.5 is the successor to ID3 and removed the constraint that features must be categorical by dynamically defining a discrete attribute (based on numeric variables) that splits the attribute's continuous value into a discrete set of intervals. C4.5 transforms the trained trees (i.e., the result of the ID3 algorithm) into sets of "if-then" rules. The accuracy of each rule is then evaluated to determine the order in which they should be applied. Removal is performed by removing the precondition of the rule if the accuracy of the rule improves without it. 
 C5.0 is the latest version of Quinlan under a private license. It uses less memory and creates smaller rule sets than C4.5, but is more accurate. 
 CART (Classification and Regression Trees) is very similar to C4.5, but differs in that it supports numeric targets (regression) and does not compute rule sets. CART builds binary trees using the function and threshold that give the largest information gain at each node.  9*)$ until the maximum allowed depth is reached, $N_m < \min_{samples}$ or $N_m = 1$. 
 1.10.7.1. Classification criteria 
 If the goal is a classification result that takes the values 0,1,…, K-1, for node m, allow 
 $$p_{mk} = 1/ N_m \sum_{y \in Q_m} I(y = k)$$ 
 be the proportion of class k observations at node m. If m is a leaf node,  predict_proba  this region is set to $p_{mk}$. The general measures of impurity are as follows. 
 Gini: 
 $$H(Q_m) = \sum_k p_{mk} (1 - p_{mk})$$ 
 Entropy: 
 $$H(Q_m) = - \sum_k p_{mk} \log( p_{mk})$$ 
 Wrong classification: 
 $$H(Q_m) = 1 - \max(p_{mk})$$ 
 1.10.7.2. Regression criteria 
 If the target is a continuous value, then for node m, the common criteria to be minimized to locate future splits are the root mean square error (MSE or L2 error), the Poisson deviation, and the mean absolute error (MAE or L1 error ). MSE and Poisson deviation set the predicted value of terminal nodes to the learned mean $\bar{y}_m$ of the node, while MAE sets the predicted value of terminal nodes to the median $median(y)_m$.  92$$ 
 Half Poisson deviation: 
 $$H(Q_m) = \frac{1}{N_m} \sum_{y \in Q_m} (y \log\frac{y}{\bar{y}_m} - y + \bar{y}_m)$$ 
 Setting  criterion="poisson"  can be a good choice if your goal is a counter or frequency (number per unit). In any case, y>=0 is a necessary condition for using this criterion. Note that it fits much slower than the MSE criterion. 
 Mean absolute error: 
 $$median(y)m = \underset{y \in Q_m}{\mathrm{median}}(y)$$ 
 $$H(Q_m) = \frac{1}{N_m} \sum{y \in Q_m} |y - median(y)_m|$$ 
 Note that it fits much more slowly than the MSE test. 
 1.10.8. Least Cost and Complexity Pruning 
 
 Least Cost and Complexity Pruning is an algorithm used to reduce a tree to avoid overfitting, described in Chapter 3 of [BRE]. This algorithm is parameterized by $\alpha\ge0$ known as the complexity parameter. The complexity parameter is used to determine the cost and complexity measure, $R_\alpha(T)$, of the given tree $T$: 
 $$R_\alpha(T) = R(T) + \alpha|\widetilde{T}|$$ 
 where $|\widetilde{T}|$ is the number of leaf nodes in $T$ and $R( T)$ is traditionally defined as the overall end node misclassification rate.  Alternatively, scikit-learn uses the weighted total admixture of end nodes for $R(T)$. As shown above, node impurity depends on the criterion. Pruning with minimal cost and complexity finds a subtree T which minimizes $R_\alpha(T)$. 
 Estimated cost complexity of one node is $R_\alpha(t)=R(t)+\alpha$. A branch $T_t$ is defined as a tree where node $t$ is its root. In general, the impurity of a node is greater than the sum of the impurities of its end nodes, $R(T_t) ccp_alpha  parameter. 
 Examples: 

 Publishing pruning decision trees with cost complexity reduction 

 Recommendations: 

 BRE L.