public CreateMLModelRequest withParameters(java.util.Map<String, String> parameters) {
        setParameters(parameters);
        return this;
    }
<p>
A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of key-value
pairs.
</p>
<p>
The following is the current set of training parameters:
</p>
<ul>
<li>
<p>
<code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input data, the
size of the model might affect its performance.
</p>
<p>
The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default value is
<code>33554432</code>.
</p>
</li>
<li>
<p>
<code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to build
the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to <code>10000</code>. The
default value is <code>10</code>.
</p>
</li>
<li>
<p>
<code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data. Shuffling the data improves a
model's ability to find the optimal solution for a variety of data types. The valid values are <code>auto</code>
and <code>none</code>. The default value is <code>none</code>. We strongly recommend that you shuffle your data.
</p>
</li>
<li>
<p>
<code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 norm. It controls overfitting the
data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature
set. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.
</p>
<p>
The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter sparingly.
</p>
</li>
<li>
<p>
<code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 norm. It controls overfitting the
data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this
parameter, start by specifying a small value, such as <code>1.0E-08</code>.
</p>
<p>
The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L2
normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter sparingly.
</p>
</li>
</ul>

@param parameters
       A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of
       key-value pairs.</p>
       <p>
       The following is the current set of training parameters:
       </p>
       <ul>
       <li>
       <p>
       <code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input
       data, the size of the model might affect its performance.
       </p>
       <p>
       The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default value
       is <code>33554432</code>.
       </p>
       </li>
       <li>
       <p>
       <code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to
       build the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
       <code>10000</code>. The default value is <code>10</code>.
       </p>
       </li>
       <li>
       <p>
       <code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data. Shuffling the data improves a
       model's ability to find the optimal solution for a variety of data types. The valid values are
       <code>auto</code> and <code>none</code>. The default value is <code>none</code>. We strongly recommend
       that you shuffle your data.
       </p>
       </li>
       <li>
       <p>
       <code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 norm. It controls overfitting
       the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse
       feature set. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.
       </p>
       <p>
       The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not
       use L1 normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter
       sparingly.
       </p>
       </li>
       <li>
       <p>
       <code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 norm. It controls overfitting
       the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If
       you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.
       </p>
       <p>
       The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not
       use L2 normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter
       sparingly.
       </p>
       </li>
@return Returns a reference to this object so that method calls can be chained together.
aws-java-sdk-machinelearning/src/main/java/com/amazonaws/services/machinelearning/model/CreateMLModelRequest.java