[MINOR][ML][DOC] Rename weights to coefficients in user guide
We should use ```coefficients``` rather than ```weights``` in user guide that freshman can get the right conventional name at the outset. mengxr vectorijk Author: Yanbo Liang <ybliang8@gmail.com> Closes #9493 from yanboliang/docs-coefficients.
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@ -71,8 +71,8 @@ val lr = new LogisticRegression()
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// Fit the model
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val lrModel = lr.fit(training)
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// Print the weights and intercept for logistic regression
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println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}")
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// Print the coefficients and intercept for logistic regression
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println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}")
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{% endhighlight %}
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</div>
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@ -105,8 +105,8 @@ public class LogisticRegressionWithElasticNetExample {
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// Fit the model
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LogisticRegressionModel lrModel = lr.fit(training);
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// Print the weights and intercept for logistic regression
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System.out.println("Weights: " + lrModel.weights() + " Intercept: " + lrModel.intercept());
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// Print the coefficients and intercept for logistic regression
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System.out.println("Coefficients: " + lrModel.coefficients() + " Intercept: " + lrModel.intercept());
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}
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}
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{% endhighlight %}
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@ -124,8 +124,8 @@ lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
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# Fit the model
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lrModel = lr.fit(training)
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# Print the weights and intercept for logistic regression
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print("Weights: " + str(lrModel.weights))
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# Print the coefficients and intercept for logistic regression
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print("Coefficients: " + str(lrModel.coefficients))
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print("Intercept: " + str(lrModel.intercept))
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{% endhighlight %}
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</div>
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@ -258,8 +258,8 @@ val lr = new LinearRegression()
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// Fit the model
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val lrModel = lr.fit(training)
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// Print the weights and intercept for linear regression
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println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}")
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// Print the coefficients and intercept for linear regression
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println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}")
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// Summarize the model over the training set and print out some metrics
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val trainingSummary = lrModel.summary
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@ -302,8 +302,8 @@ public class LinearRegressionWithElasticNetExample {
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// Fit the model
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LinearRegressionModel lrModel = lr.fit(training);
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// Print the weights and intercept for linear regression
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System.out.println("Weights: " + lrModel.weights() + " Intercept: " + lrModel.intercept());
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// Print the coefficients and intercept for linear regression
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System.out.println("Coefficients: " + lrModel.coefficients() + " Intercept: " + lrModel.intercept());
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// Summarize the model over the training set and print out some metrics
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LinearRegressionTrainingSummary trainingSummary = lrModel.summary();
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@ -330,8 +330,8 @@ lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
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# Fit the model
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lrModel = lr.fit(training)
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# Print the weights and intercept for linear regression
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print("Weights: " + str(lrModel.weights))
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# Print the coefficients and intercept for linear regression
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print("Coefficients: " + str(lrModel.coefficients))
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print("Intercept: " + str(lrModel.intercept))
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# Linear regression model summary is not yet supported in Python.
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