在logistic regression的优化过程中,目标loss最小(maximum likelihood),这样会倾向于让w变大,使得所有样本的概率尽可能接近1,但这样实际上是overconfident。 w变大,让样本概率接近1,如下图: 这两种overfitting的表现都是w较大。 而linear regression只有第一种overfitting,所以说overfitting in logistic regression is ‘...
4.有没有第三种方式来处理missing data? adapt learning algorithm to be robust to missing values.修改机器学习算法 以决策树为例: 5.那么如何修改决策树算法来支持missing data呢? 在选择feature时候,不仅要选择feature,还要选择如果该feature missing的话,进入哪个branch classification error最小。
Learn about classification in machine learning, looking at what it is, how it's used, and some examples of classification algorithms.
I also tried to use cross_val_score for the classification to get the evaluation right away but ran into another error (from cross_val_score line): File "_csparsetools.pyx", line 20, in scipy.sparse._csparsetools.lil_get1 File "_csparsetools.pyx", line 48, in s...
Supervised and unsupervised machine learning methods make a classification decision based on feature inputs.
Std Error. This can measure the accuracy of the coefficient estimates Z value. Z value tells us how many standard deviations is the coefficient estimate away from a standard normal curve with 0 mean and 1 sd. In this example, we can find that z value for intercept is -1.471, meaning tha...
The question depends on one core conception that is omnipresent in the studying and career of machine learning,variance and bias tradeoff. If K classes share the common covariance matrix, the LDA has a linear decision boundary, which means that the coefficients of LDA model should be linear. I...
Join Stack Overflow’s CEO and me for the first Stack IRL Community Event in... User activation: Learnings and opportunities Related 5 Precision and Recall if not binary 2 Sklearn classification report is not printing the micro avg score for multi class classification model ...
In machine learning and statistics, classification is the problem of identifying which of a set of categories a new observation belongs to, on the basis of a training set of data containing observations whose category membership (label) is known. ...
A benefit of RMSE is that the units of the error score are in the same units as the predicted value. An algorithm that is capable of learning a regression predictive model is called a regression algorithm. Some algorithms have the word “regression” in their name, such as linear regression...