Spark 2.x：使用word2vec或HashingTF运行逻辑回归的预测结果疑问求助

Hey there! Let's break down your Spark ML pipeline issues step by step, based on what you've shared.

• Your guess about hash collisions is totally valid! HashingTF maps words to a fixed-dimensional space via a hash function. When you set setNumFeatures to such a tiny value (like 10), it's almost guaranteed that different words will get hashed to the same feature index—this is exactly hash collision.
• This collision messes up the true meaning of features: for example, key words that should distinguish positive and negative samples might get lumped together with irrelevant words in the same feature dimension. The model ends up learning wrong correlations, which leads to the unexpected prediction for the sample with id=5.
• Fix suggestions: If you stick with HashingTF, set setNumFeatures to a much larger value (like the default 2^18=262144, or adjust based on your vocabulary size) to drastically reduce collision chances. For collision-free feature mapping, switch to CountVectorizer or TF-IDF Vectorizer—they assign unique indices to each word.

Since you mentioned the prediction result for id=5 is still off (even though you didn't finish the description), here are common reasons this might happen:

• Insufficient training data for Word2Vec: Word2Vec needs a decent amount of corpus to learn meaningful word embeddings. If your training dataset is small, the learned vectors might not capture enough semantic detail to tell different words apart, making it hard for the model to judge the id=5 sample correctly.
• Poor Word2Vec parameter settings: If vectorSize (the dimension of word vectors) is set too small, it can't hold enough semantic information. Or if minCount (the minimum number of times a word must appear to be included) is set too high, key words in the id=5 sample might get filtered out, leaving an empty or useless feature vector.
• Sample-specific issues: The id=5 sample might have noise (like ambiguous text, wrong labels) or be very different from the training data distribution. No matter what feature extractor you use, the model will struggle to predict it accurately.

• First, check the text content of the id=5 sample: Compare its words with positive/negative samples in the training set—are there special words or formatting issues?
• Inspect feature outputs: Print the feature vectors of the id=5 sample processed by HashingTF and Word2Vec, then compare them with the feature distribution of positive/negative training samples. This will show if the features lack differentiation.
• Tweak model parameters: For Word2Vec, try increasing vectorSize (e.g., 100 or 200) and lowering minCount. For Logistic Regression, adjust regParam (regularization parameter) to avoid overfitting or underfitting.

内容的提问来源于stack exchange，提问作者creativespark