2. 模型改进 我的做法进行预测后提交到kaggle后的分数是0.191多,看了下高分的做法,发现不仅可以使用原材料中给出的特征,还可以根据实际经验将特征进行组合或事丢弃,这方面深挖了也会涉及很多东西,以后有时间接着更新
The generalized linear model has the same implementation as the general linear regression, pointed above, because the general linear model is just a special case. The generalized linear model allows applying a function on the output and that's about it....
He elegido el dataset de precios de viviendas en California, disponible en Kaggle, como punto de partida para este proyecto. Este conjunto de datos proporciona una excelente oportunidad para explorar y aplicar técnicas de regresión en un contexto real. Link del dataset: Dataset de Kaggle ...
4. Softmax (softmax.py) For the logistic regression classifier, multi-class prediction is difficult, as it requires a one-vs-one or one-vs-rest classifier for every class. Therefore, you only need to use logistic regression on the Rice dataset. The top-level notebook (CS 444 Assignment-...
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To get a practical sense of multiple linear regression, let's keep working with our gas consumption example, and use a dataset that has gas consumption data on 48 US States. Note:You can download the gas consumption dataset onKaggle. You can learn more about the details on the datasethere...
DP Test of significance and p-values for regression coefficients for simulated normal data Script 1. Noisy SGD with clipping. To be used with health insurance cost data from https://www.kaggle.com/mirichoi0218/insurance. First conducts exploratory data analsis and pre-processing. Then implements...
This Kaggle competition requires you to fit/train a model to the providedtrain.csvtraining set to make predictions of house prices in the providedtest.csvtest set. We present an application of theget_regression_points()function allowing students to participate in this Kaggle competition. It will:...
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