Question

我使用sklearn_pandas和sklearn创建了一个ML管道。看起来像这样。

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    background:url('http://spacetelescope.github.io/understanding-json-schema/_static/pass.png');
    background-repeat:repeat-y;
    height:30px;
    text-indent:20px;
}
</style>

<div id="myDiv">
    <input id="info" type="button" value="Имате Въпрос?" class="showButon" />
</div>

(function(){
var button = document.getElementById("info");
    var myDiv = document.getElementById("myDiv");

    function toggle() {
        if (myDiv.style.visibility === "hidden") {
            myDiv.style.visibility = "visible";
        } else {
            myDiv.style.visibility = "hidden";
        }
    }

    button.addEventListener("click", toggle, false);
})()

我喜欢我得到的模型和日志损失值。如何使用此管道预测我的测试集？

当我执行pipe.predict（testX [features]）时，我收到一条错误消息：

features = ['ColA','ColB','ColC']
labels = 'ColD'

mapper = sklearn_pandas.DataFrameMapper([
    ('ColB',sklearn.preprocessing.StandardScaler()),
    ('ColC',sklearn.preprocessing.StandardScaler())
])
pipe = sklearn.pipeline.Pipeline([
        ('featurize',mapper),
        ('imputer',imputer),
        ('logreg',sklearn.linear_model.LogisticRegression())
])
cross_val_score = sklearn_pandas.cross_val_score(pipe,traindf[features],traindf[labels],'log_loss')

我检查了我的测试集。看起来很好。

Answer 1

您必须先安装管道，就像您适合任何型号/变压器一样：

pipe.fit(traindf[features], traindf[labels])

如何使用ML sklearn管道进行预测？

1 个答案: