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[MINOR][ML][DOC] Rename weights to coefficients in user guide

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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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Yanbo Liang 2015-11-05 08:59:06 -08:00 committed by Xiangrui Meng
parent 77488fb8e5
commit 72634f27e3
1 changed files with 12 additions and 12 deletions
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24
docs/ml-linear-methods.md
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@ -71,8 +71,8 @@ val lr = new LogisticRegression()
// Fit the model
val lrModel = lr.fit(training)
// Print the weights and intercept for logistic regression
println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}")
// Print the coefficients and intercept for logistic regression
println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}")
{% endhighlight %}
</div>
@ -105,8 +105,8 @@ public class LogisticRegressionWithElasticNetExample {
// Fit the model
LogisticRegressionModel lrModel = lr.fit(training);
// Print the weights and intercept for logistic regression
System.out.println("Weights: " + lrModel.weights() + " Intercept: " + lrModel.intercept());
// Print the coefficients and intercept for logistic regression
System.out.println("Coefficients: " + lrModel.coefficients() + " Intercept: " + lrModel.intercept());
}
}
{% endhighlight %}
@ -124,8 +124,8 @@ lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
# Fit the model
lrModel = lr.fit(training)
# Print the weights and intercept for logistic regression
print("Weights: " + str(lrModel.weights))
# Print the coefficients and intercept for logistic regression
print("Coefficients: " + str(lrModel.coefficients))
print("Intercept: " + str(lrModel.intercept))
{% endhighlight %}
</div>
@ -258,8 +258,8 @@ val lr = new LinearRegression()
// Fit the model
val lrModel = lr.fit(training)
// Print the weights and intercept for linear regression
println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}")
// Print the coefficients and intercept for linear regression
println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}")
// Summarize the model over the training set and print out some metrics
val trainingSummary = lrModel.summary
@ -302,8 +302,8 @@ public class LinearRegressionWithElasticNetExample {
// Fit the model
LinearRegressionModel lrModel = lr.fit(training);
// Print the weights and intercept for linear regression
System.out.println("Weights: " + lrModel.weights() + " Intercept: " + lrModel.intercept());
// Print the coefficients and intercept for linear regression
System.out.println("Coefficients: " + lrModel.coefficients() + " Intercept: " + lrModel.intercept());
// Summarize the model over the training set and print out some metrics
LinearRegressionTrainingSummary trainingSummary = lrModel.summary();
@ -330,8 +330,8 @@ lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
# Fit the model
lrModel = lr.fit(training)
# Print the weights and intercept for linear regression
print("Weights: " + str(lrModel.weights))
# Print the coefficients and intercept for linear regression
print("Coefficients: " + str(lrModel.coefficients))
print("Intercept: " + str(lrModel.intercept))
# Linear regression model summary is not yet supported in Python.